Local user authentication with neuro and neuro-mechanical fingerprints

ABSTRACT

In accordance with one embodiment, a method for locally verifying the identification of a user with an electronic device is disclosed. The method includes regenerating a neuro-mechanical fingerprint (NFP) in response to a micro-motion signal sensed at a body part. In response to a plurality of authorized user calibration parameters, a match percentage of the neuro-mechanical fingerprint is determined. The match percentage is determined without the use of a calibration NFP that was previously used to generate the user calibration parameters. Access to the electronic device and its software applications is then controlled by the match percentage. If the match percentage is greater than or equal to an access match level, access to the electronic device is granted. If the match percentage is less than the access match level, access is denied. Subsequent access requires further regeneration of the NFP and a determination of its match percentage in response.

CROSS REFERENCE TO RELATED APPLICATIONS

This United States (U.S.) patent application claims the benefit of U.S.Provisional Patent Application No. 62/112,153 entitled LOCAL USERAUTHENTICATION WITH NEURO-MECHANICAL FINGERPRINTS filed on Feb. 4, 2015by inventors Martin Zizi et al.

FIELD

The embodiments relate generally to user identification andauthentication.

BACKGROUND

Wide area network connections over the Internet have interconnected manyelectronic devices, such as computers and mobile smart phones, to remoteservers and remote storage devices so that cloud computer services canbe provided. More electronic devices are poised to be interconnectedover the Internet as wireless radio transmitter/receivers are added tothem.

Access by a user to some electronic devices and databases is often by alogin name and password. As more portable electronic devices are used,such as laptop computers and mobile smart phones, in a highly mobilecomputing environment, correct authentication of people and devicesbecomes important to ascertain authorized use and lower risks linked todata misrouting. For example, as more mobile health electronic devicesare introduced, privacy of the captured health data by the mobile healthdevices becomes important. As more banking and payments are made usingmobile electronic devices, authorized use becomes important.

Authentication of a user using a local electronic device is oftenperformed by a remote server. Software applications executed by thelocal electronic device often save the login name and password of theuser to make an electronic device and its software applications easierto use. Protecting the local access of the user's electronic device hasbecome more important in protecting the user and his/her login name andpassword when an electronic device is lost or stolen. With electronicdevices now being used to make payments like credit card transactions,protection of local access to a user's electronic device has become evenmore important.

Referring now to FIG. 1, various known behavioral and anatomicalidentification methods are illustrated. Behavioral identificationmethods are linked to what the user does or his/her habits. Knownanatomical identification methods are linked to physical features of theuser, such as fingerprints, iris eye scans, veins, facial scans, andDNA.

Biometrics are being used to better authenticate a user to providegreater protection of mobile electronic devices. Biometrics have beenpursued with anatomical based approaches (i.e., physical features suchas fingerprints, iris eye scans, veins, facial scans, DNA, etc.) andhabit (behavioral) approaches (typing or keystrokes, handwritingsignatures, voice or vocal inflections). For example, an anatomicalaspect of the user, such as a fingerprint, is used to locallyauthenticate a user and restrict access to the electronic device. Asanother example, hand morphology or the position of the veins can beused with an image analysis to locally authenticate a user and restrictaccess to the electronic device.

Behavioral aspects of a user may also be used to better authenticate auser to provide greater protection of mobile electronic devices. Thebehavioral aspect involves a personal profile of a user's unique habits,tastes, and behavior. For example, the way a user signs his/hersignature is a unique behavioral aspect that can be used to verify auser's identity.

However, neither known anatomical nor known behavioral aspects of useridentification are foolproof. For example, known user data used forcomparison can be stolen from a server and used without the user'sknowledge. Additional hardware is often required to capture biometricsfrom the user that leads to increased costs. With known added hardware,risks may be increased where a user may be forced by another tounwillingly capture biometric data. Behavioral aspects may be somewhatintrusive and raise privacy issues. Sensing for behavioral aspects isoften performed with software that requires a system to remain poweredon, consuming energy that is often stored in rechargeable batteries.Moreover, biometrics using behavioral aspects often require the addedexpense of maintaining remote centralized databases.

BRIEF SUMMARY

Using biometrics with known anatomical and/or known behavior aspects ofa user for identification have some disadvantages.

There is a need for a user authentication solution that is easy to use,de-centralized so it can be used locally, is substantially fail-safefrom predators, consumes low power for battery applications, andrespects a user's privacy by protecting a user's data so that it isreadily adoptable.

The user authentication solution disclosed herein provides fail-safeauthentications in a decentralized mobile environment while protecting auser's privacy and data. The user authentication solution disclosedherein employs a different form of biometric that is tamper resistant.

The user authentication solution disclosed herein can employ 3-D sensorsand signal processing to capture signals (“micro-motion signals”) thatrepresent neuro-muscular micro-functions of a user, that are translatedinto well defined micro-motions. Signal processing algorithms andfeature extraction are then used to capture unique signal features inthe micro-motion signals. These unique signal features associated withthe neuro-muscular micro-motions of a user, can be used to uniquelyidentify a user, somewhat similar to a fingerprint. Accordingly, theseunique signal features associated with the neuro-muscular micro-motionsof a user are referred to herein as “neuro-mechanical fingerprints”(NFPs). The neuro-mechanical fingerprints can be used for local userauthentication at a mobile electronic device, such as a laptop computeror smartphone, or at other types of electronic devices. Using the NFP ofa user, captured by sensors and extracted by algorithms, avoidsprofiling and storing a user's habits or patterned behavior.

The embodiments of the user authentication solution employing sensorsand signal processing algorithms to generate and regenerateneuro-mechanical fingerprints (NFPs) of an authorized user are disclosedby the attached figures and the detailed description herein.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIG. 1 is a background figure illustrating various behavioral andphysiological identification methods.

FIGS. 2A-2D illustrate examples of Poincare' phase plot diagrams showinggravity corrected three-dimensional accelerometry data captured fromdifferent users.

FIG. 3A is diagram illustrating remote authentication of a user over theInternet with a server and a storage device of a storage area network(cloud storage).

FIG. 3B is diagram illustrating local authentication of a user at alocal electronic device.

FIG. 4 is a chart illustrating authentication techniques that may beused with NFP authentication to provide multi-factor authentication.

FIG. 5 is a chart to compare various biometric identification techniquesagainst NFP authentication.

FIG. 6 is a table of tremor types and associated frequencies andconditions.

FIG. 7 is a functional block diagram of an example of an electronicdevice including sensors to capture micro-motion signals from a user andan NFP authentication system to control access in response to themicro-motion signals.

FIG. 8A-8C are NFP authentication systems with differing sensors tosense micro-motions from a finger or from a hand.

FIG. 9 is a waveform diagram of acceleration measured at a user's handto show the difference between macro-motions and micro-motions.

FIG. 10A is a diagram illustrating device orientation and worldorientation.

FIG. 10B is a diagram illustrating use of an eigenvector to transform3-D data samples in a dataset from device orientation to worldorientation.

FIG. 11A is a functional block diagram of a band-pass filter to filterout signals outside the frequency range of a desired tremor to capturemicro-motion signals.

FIG. 11B is a functional block diagram of a high pass filter and a lowpass filter to achieve the same results of the band pass filter of FIG.11A.

FIG. 12A is a functional block diagram of a gravity high-pass filter tofilter the influence of gravity.

FIG. 12B is a diagram illustrating a coordinate transformation of datasamples in a data set to move a center of gravity to the origin of thethree axes to eliminate the influence of gravity.

FIG. 13A is a waveform diagram of a raw unfiltered sample set ofacceleration data signals in three dimensions from a 3D accelerometer.

FIG. 13B is a waveform diagram of a 3-D micro-motions signal afterpreprocessing, band pass filtering, and gravity compensation of the rawsignal waveforms shown in FIG. 13A.

FIG. 14A is a functional block diagram of an NFP authenticationcontroller.

FIG. 14B is a functional block diagram of the NFP authenticationclassifier shown in FIG. 14A

FIG. 15 is a graph of access and recalibration control by theauthentication controller shown in FIG. 14A.

FIG. 16 is a plot of power spectral density in a micro-motions signalassociated with a tremor.

FIG. 17A is a plot of a CEPSTRUM waveform generated by performing aCEPSTRUM analysis on the micro-motions waveform shown in FIG. 13B.

FIG. 17B is a magnified view of a portion of the plot of the CEPSTRUMwaveform shown in FIG. 17A.

FIGS. 18A-18B illustrate examples of different NFPs for a single axisbased on quefrency for two different users.

FIGS. 19A-19B illustrate examples of different NFPs for a single axisbased on time for two different users.

FIG. 20 illustrates a hidden Markov model that may be used as the NFPclassifier model to determine a match percentage from an NFP andauthorized user calibration parameters.

DETAILED DESCRIPTION

In the following detailed description of the embodiments, numerousspecific details are set forth in order to provide a thoroughunderstanding. However, it will be obvious to one skilled in the artthat the embodiments may be practiced without these specific details. Inother instances well known methods, procedures, components, and circuitshave not been described in detail so as not to unnecessarily obscureaspects of the embodiments.

The embodiments include methods, apparatus, and systems for forming andutilizing neuro-mechanical fingerprints (NFPs) to identify andauthenticate a user.

INTRODUCTION

Certain user motions are habitual or part of a user's motion repertoire.A user signing a document, for example, is a contextual motion that auser develops with behavioral habits. The motions usually analyzed of asigned signature are the macro-motions or large scale motions that auser makes with a writing instrument. For example, from the largemotions of a signed signature one may determine with ones eyes whetherthe writer was left handed or right handed, for example.

While these large motions may be useful, there are also micro-motions(very small motions) that a user makes when signing, making othermotions, or simply at rest making no motion. These micro-motions areneuro-derived or neuro-based and invisible to the eyes. Thesemicro-motions of a user are due to the unique neuromuscular anatomy ofeach human being and may be also referred to herein as neuro-derivedmicro-motions. These micro-motions are also linked to the motor controlprocesses from the motor cortex of an individual down to his/her hands.With one or more sensors, signal processing algorithms, and/or filters,electronic signals (“motion signals” and “micro-motions signals”) can becaptured that include the neuro-derived micro-motions of a user. Ofspecific interest are micro-motion electronic signals that represent themicro-motions of the user within the motion signals.

Micro-motions of a user are linked to the cortical and subcorticalcontrol of the motor activities in the brain or elsewhere in the nervoussystem of a human body. Like a mechanical filter, the specificmusculoskeletal anatomy of an individual can affect the micro-motions ofa user and contribute to the motion signals that include themicro-motions of a user. The motion signals captured from a user canalso reflect part of the proprioceptive control loops that include thebrain and proprioceptors that are present in a user's human body.

When motion signals are analyzed appropriately for micro-motion signalsrepresenting micro-motions of users, the resulting data can yield uniqueand stable physiological identifiers, more specifically neurologicalidentifiers, that can be used as unwritten signatures. These uniqueidentifiers are a user's neuro-mechanical fingerprints. Neuro-mechanicalfingerprints may also be referred to herein as NeuroFingerPrints (NFPs).

The motions of a user, including a user's macro-motions and a user'smicro-motions, can be captured by various electronic sensors. Forexample, an electronic touch sensor or an electronic accelerometer maybe used to capture the macro and micro motions of a user. U.S. patentapplication Ser. No. 13/823,107 filed by Geoff Klein on Jan. 5, 2012,incorporated herein by reference, describes how an accelerometer may beused to unobtrusively capture motion data of a user over time whenoperating an electronic device and generate a motion-repertoire that canbe used to recognize a user. U.S. patent application Ser. No. 13/823,107makes use of repertoires or motion habits. The embodiments disclosedherein do not build nor rely on motion repertoires. The embodimentsdisclosed herein extract signals linked to quality control mechanisms ofthe nervous system. U.S. patent application Ser. No. 13/823,107 ignoresthose user's neuro-derived micro-motions that are of interest to theembodiments disclosed herein. Moreover, in the generation ofneuro-mechanical fingerprints, the macro-motion signals are suppressedor filtered out to capture the micro-motions signal component. Whenusing a three-dimensional accelerometer to capture a user'smicro-motions, the macro-motions signal needs filtering out from themicro-motion signal data. The vector due to the force of gravity is alsoignored in U.S. patent application Ser. No. 13/823,107. When using athree-dimensional accelerometer, the force of gravity is compensated foror filtered out in the generation of the micro-motions signal.

Referring now to FIGS. 2A-2D, examples of three-dimensional Poincare'phase plot diagrams for four different users are shown. Eachthree-dimensional Poincare' phase plot diagram shows a pattern 200A-200Dof gravity corrected three-dimensional accelerometry data.

The raw accelerometry data was obtained over the same period of timewith users doing macro-motions with their hands using the sameelectronic device. The electronic device has three-dimensionalaccelerometer sensors to capture raw accelerometry data in the threedimensions of X, Y, and Z.

Signal processing is performed on the raw accelerometry data to filteror suppress unwanted signals, correct for gravity and extract thesignals (micro-motion signals) that represent the neuro-derivedmicro-motions of a user's hand or finger. The three dimensions ofmicro-motion signals x(t), y(t), z(t) over the sample period of time fora user can be further processed into phase(t), y(t), z(t) and plotted ina three-dimensional Poincare' phase plot diagram.

As can be readily seen in FIGS. 2A-2C, the patterns 200A-200D in each ofthe Poincare' phase plot diagrams generated from the neuro-derivedmotion of the users are substantially different. For example, a centerof mass 202A-202D of each pattern 200A-200D differs. Othercharacteristics of each pattern 200A-200D also differ for each user.Thus, the pattern of neuro-derived motion is unique to each user and canbe used to uniquely identify a user.

The unique patterns 200A-200D in the generated Poincare phase plots andthe NFP are normally stable. Thus, normally they can be repeatedlysensed over sample periods of time each time a user touches or moves thesensor and then compared with an initial calibrated NFP using analgorithm to authenticate the identity of a user. However, if there is aneuromuscular disease that is progressing, the unique pattern 200A-200Dfor a user is less stable. If this is the case, the neurologicalalgorithm can be periodically recalibrated to compensate for diseaseprogression. For example, an elderly person with a neuromuscular diseasecan easily recalibrate the neurological algorithm on a weekly basis,depending on a drift cutoff relative to an initial calibration for theNFP. As another example, a user who is being treated withmotion-altering medications can also recalibrate the NFP neurologicalalgorithm on a periodic basis. For most users, recalibration of the NFPneurological algorithm after an initial calibration will be infrequent.

The analysis of motion signals for micro-motions employs a neurologicalalgorithm to obtain a neuro-mechanical fingerprint (NFP) of a user.Accordingly, this neurological algorithm may be referred to herein as anNFP neurological algorithm.

NFP Neurological Algorithm

A NFP neurological algorithm is not an algorithm that uses neuralnetwork training, classifiers, or deep learning. The NFP neurologicalalgorithm is an algorithm that specifically collects, isolates, andanalyzes signals from the human nervous system and its interactions withparts of the body connected to the nervous system, such as the muscularsystem or the gland cells, the skin. Activities of interest to analyzesignals for micro-motions include the motor control stemming out of ournervous system, the sensorial inputs, and stress responses that areuniquely linked to the anatomy and nervous system of each person.

Consider, for example, a user who moves his or her hand. A kinematicalgorithm may record the physical motion over time and analyze it forits velocity, distance, direction, and force, for example. Aneurological algorithm collects, isolates, and analyzes the neuralactivities over time linked to the motion of the hand from an electronicsignal, such as a motion signal including micro-motion signals, withoutperforming a brain scan with a brain scanner.

While kinematic motions of body parts can be analyzed for micro-motionsusing an electronic accelerometer, for example, an electronic touchsensor, such as a touch pad, can be used to analyze the touch of one ormore fingers to the touch sensor. The touch sensor can be used togenerate a micro-motion signal representing the micro-motions over timeat a finger touching the touch sensor. A touch sensor can generatethree-dimensional signals representing the X and Y position of a fingeragainst the touch sensor and a Z position representing the pressureapplied to the touch sensor. Micro-motions may be included as part ofeach of the three-dimensional position signals. With a touch sensor,gravity is typically not a factor that needs correction.

By focusing on micro-motion signals and not macro-motion signals, atouch sensor may be used with a neurological algorithm to better emulatea human cognitive interface in a machine. This can improve man-machineinterfaces. For example, consider a human cognitive interface between ahusband and wife or closely-knit persons. When a husband touches hiswife on the arm, the wife can often times recognize that it is herhusband touching her just from the feel of that touch, because she isfamiliar with his touch. If the touch feels unique, a human can oftenrecognize what it is that is touching him/her just from that uniquefeel.

Touch Recognition

The technique of using of a touch sensor to sense and capturemicro-motions and a neurological algorithm to analyze an NFP may bereferred to herein as touch recognition. A system that includes touchrecognition to authenticate and/or identify a user may be referred toherein as a touch recognition system. With respect to FIG. 1,touch-recognition using NeuroFingerPrints is a physiological useridentifier. Touch recognition is not an anatomical user identifier.Touch recognition is not a behavioral user identifier. Contrary toanatomical identification methods, which are fixed attributes of a user,physiological identification methods are linked to the functional bodyof a user. Behavioral identification methods are linked to what the userdoes or his/her habits.

Using a touch sensor and a neurological algorithm has intrinsicadvantages in user authentication. The user interface for userauthentication can be intuitive and linked to the spontaneous behaviorof the human user with only minimal training, if any training is neededat all. The user experience of the user authentication process can befrictionless. The user need not perform any specific user authenticationtask over and above those ordinarily associated with the use of theuser's electronic device. For example, after calibration, a user mayonly need to hold his/her smart phone, such as to make a telephone call,so that an accelerometer and a neurological algorithm perform the userauthentication process.

NeuroFingerPrints can be captured non-intrusively by a touch sensor andthe NFP neurological algorithm. NeuroFingerPrints can be isolated fromother user motions. Accordingly, one or more touch sensors used tocapture keystrokes or key depressions may also be used to capturemicro-motions of a user and generate micro-motion signals and theNeuroFingerPrints in response thereto. For example, a user may only needselect one or more buttons (e.g., dial a telephone number for atelephone call, enter a personal identification number, select afunction button or power button) so that one or more touch sensorsthere-under and a neurological algorithm perform the user authenticationprocess. With little to no change in the user interface, one or moretouch sensors associated with a keypad or button can be used to capturemicro-motions and generate micro-motion signals as a user presses thekeypad or button. Without any change in user experience, aNeuroFingerPrint (NFP) can be generated by the NFP neurologicalalgorithm while a user's personal identification number (PIN) isentered, for example. Alternatively, a NeuroFingerPrint (NFP) can begenerated by the NFP neurological algorithm while a power button or afunction button (e.g., home button) is pressed for example. In thiscase, the NeuroFingerPrint (NFP) can be used for user authenticationwithout a PIN number. Alternatively, with little to no user interfacechange, a NeuroFingerPrint (NFP) can be used conjunctively with a PINnumber for user authentication.

Local and Remote User Authentication

Reference is now made to FIGS. 3A-3B. FIG. 3A illustrates remoteauthentication of a user. FIG. 3B illustrates local authentication of auser.

Touch recognition facilitates local authentication of a user at a localelectronic device, such as shown in FIG. 3B. NFP user authenticationdoes not require the use of a large remote database. An NFP userauthentication system can be self-contained within the local electronicdevice, such as a mobile smartphone. Data for user authenticationneedn't be stored in and accessed from a storage device of a storagearea network (the “cloud”) over the Internet, as may be required forremote authentication. A login ID and password for user authenticationneed not be sent over the Internet to a server to authentic a user, asmay be required for remote authentication. Touch recognition isdecentralized and can be self contained within the local electronicdevice.

With remote authentication of a user, such as shown in FIG. 3A, a useris open to the Internet and may be subject to risks associated with it.With local authentication, such as shown in FIG. 3B, a user may beclosed off from the Internet during user authentication to provide addedsecurity. A token may be passed out from the local electronic devicerepresenting local authentication of a user to a local server or aremote server over an internet cloud such as shown in FIG. 3B.

While touch recognition facilitates local authentication, touchrecognition may be readily combined with a remote authenticationtechnique or another local authentication technique to provideadditional levels of security.

Multi Factor Authentication and NeuroFingerPrints

Referring now to FIG. 4, a user authentication system may employmultiple factors to authenticate a user. Multi factor authenticationcombines two or more authentication techniques. Previously, multi-factorauthentication was not user-friendly so it was avoided. User-friendlyauthentication methods were often preferred instead, but led tocompromised protection.

As mentioned herein, touch recognition or recognition using anaccelerometer may be combined with a remote authentication technique oranother local authentication technique so that multi-factorauthentication is more user-friendly.

Touch recognition may be combined with a password, a personalidentification number, a pattern, or something else a user knows orremembers. Alternatively, touch recognition may be combined with atoken, a smart card, a mobile token, an OTP token, or some other userauthentication device a user has. Alternatively, touch recognition maybe combined with an anatomical or behavioral user biometric. Forexample, neurological based touch recognition may be readily combinedwith finger print recognition to provide a user-friendly multi factorauthentication system.

The banking industry is particularly interested in avoiding an extraburden on the user when user authentication is required. Moreover, thebanking industry is interested in high performance user authenticationsystems that provide reliable user authentication and are difficult tocircumvent. Neurological based touch recognition can be readily combinedwith other authentication techniques with minimal burden on the user.Moreover, the neurological based touch recognition that is disclosedherein can be substantially reliable and very difficult to circumvent.

Comparison of Biometric Identification Techniques

Referring now to FIG. 5, a chart of a comparison matrix of variousbiometric identification technologies is illustrated. A relative rankingis used to compare user authentication with NFPs to other biometric useridentification techniques. The relative ranking is based on seven ratedcriteria. Each criterion can be rated high (H), medium (M), or low (L)for each biometric identification technique. From left to right, theseven rated criteria are permanence, collectability, performance,acceptability, circumvention, uniqueness, and universality. The use ofNFPs ranked highest over the other biometric identification techniquesfor the following reasons.

NFP is a highly ranked universal technology. Each and every human beingthat would be a user has a unique nervous system from which NFPs can becaptured.

NFP is highly unique user authentication technology differentiatingbetween users. The NFP reflects a user's motor control of the user'sbody. Motor control is linked to the brain activity controlling themuscular tone in the hands. Even twins will have different motor controlbecause the will undergo different life's experiences and have differentmotor skill training. For example, twins will learn to ride a bicycledifferently, learning different motor control over muscles that will bereflected in differing NFPs.

NFP is a relatively highly permanent or stable technology because it islinked to each person's anatomy. Evolutive processes, such as aneurological disease that can affect the stability of NFPs, are rare.Even in those rare cases, the neurological evolution process is usuallysufficiently slow such that an NFP user authentication system can berecalibrated. The NFPs for touch recognition differs for a user's lefthand and a user's right hand. However, they are each user specific NFPs.A user can be consistent in what hand/finger that he/she uses tohold/touch an accelerometer enabled device/touch sensor enabled device.Alternatively, multiple NFPs for the same user can be calibrated withthe NFP user authentication system. In this case, the touch of eitherhand or multiple fingers may authenticate a user of an electronicdevice.

Some neurotropic medications can alter the scanned NFP. However, the NFPuser authentication system can be re-calibrated to compensate.

Alcohol can temporarily alter a scanned NFP so that it does not match acalibrated NFP. This could be advantageously used to prohibit driving acar under the influence of alcohol. For example, the NFP touchtechnology could be implemented within a car key or a car's startbutton. If the scanned NFP is unaffected by alcohol, the NFP touchtechnology allows key or start button to start up the engine to drivethe car. If the scanned NFP is affected by a user drinking alcohol, theNFP touch technology can prohibit the key or start button from startingthe engine of the car.

NFPs are relative easy to collect or capture because accelerometers havebecome common in mobile devices. Accelerometer values are frequentlysampled with a clock at or around a microsecond to capture micro-motionsof a user. Moreover, accelerometers are relatively sensitive.Accelerometers often can sense submicro-levels of accelerations suchthat they are capable of capturing micro-motions of a user's hand forexample. Various three dimensional accelerometers may be used to capturemicro-motions of a user's hand. Various touch sensors may be used tocapture micro-motions and the type, whether capacitive or resistive, isnot relevant.

The performance of NFP authentication with NFPs is high. Usingnon-optimal math algorithms as the neurological algorithms can achieverecognition success rates between 93 percent and 97 percent. Moreappropriate and accurate mathematic algorithms that consider variablesof time and trajectory can achieve higher recognition success rates toreach 100 percent recognition of a proper user. An alternate userauthentication system, such as a PIN, may be used in parallel with NFPauthentication in case the NFP authentication system fails to recognizea proper user. In comparison, most biometrics (except for realfingerprints or iris scan) have low performance with higher failurerates that can range between 18-20% providing between 80 and 82successful recognition rates.

NFP technology should have a high level of acceptability because it is auser-friendly authentication method. An NFP user authentication systemcan be fairly transparent or friction-less. However, acceptability isalso subject to how the NFP user authentication system is designed andmarketed, along with how it is supported by appropriate safety measuresand independent auditing.

NFPs are difficult to circumvent so they have a high rating for thecircumvention factor. Hacking, spoofing or reverse-engineering to gainaccess can be made nearly impossible when the NFP authentication systemresides in the electronic device and is unavailable to the Internet.

A calibration NFP file is initially generated for a user by the NFP userauthentication electronically. The calibration NFP file is typicallyencrypted and saved locally on the electronic device and is thereofunavailable to the Internet. However, when stored elsewhere, such as astorage device in a storage area network or a storage device associatedwith an authentication server, the calibration NFP file should beencrypted to provide greater security.

NFP user authentication does not simply perform a file comparison togrant access. The NFP has to be regenerated from the user's body in realtime to gain access. This avoids a hacker from using a stolen file togain access and authorization to the electronic device by using asurrogate misleading input, or a spoofing or a phishing event forinformation. To circumvent NFP user authentication at the service level,the hacker would have to concurrently make the data acquisition inparallel to the authorized user, while the user is accessing a servicethat requires authentication or login. To circumvent NFP userauthentication at the device level, a hacker would have to emulate theneurological control of the authorized user to obtain the NFP data andthen enter into it into the device to gain access. A hacker could stealthe calibrated NFP value from the authorized user's device, but then thehacker would have to impossibly regenerate the user's micro-motion inputinto a second electronic device with the same calibrated NFPauthentication system. Accordingly, an electronic device with an NFPauthentication system can be extremely difficult to circumvent. A mobileelectronic device with an NFP authentication system can ultimately betrusted as a user's gatekeeper providing real-time validatedidentification.

Only when a user utilizes the electronic devices is a scanned NFPsubsequently generated in real time, with the calibrated NFP onlyadjusting the calculations. Without the proper user that generated thecalibrated NFP file, the NFP authentication system denies local accessto the electronic device. If the proper user is not alive, the NFPauthentication system will deny access because the micro-motion signalsof the proper user cannot be generated. Accordingly, circumvention ofthe NFP authentication technology is difficult. By comparison, classicalfingerprints have a high difficulty level of circumvention, however,even classical fingerprints can be stolen and reverse-engineered tologin into a fingerprint authentication system.

Overall, NFP technology as the biometric for a user authenticationsystem is ranked highly when compared to other biometric technologies.

Privacy Protection

Mobile electronic devices are being used with health and fitnessapplications. In some cases, they may be used as the user interface toconnected medical devices. Rules and laws in a number of countriesregulate privacy of a user's medical condition. Accordingly, protectionof a user's medical condition and medical data has become increasinglyimportant.

An NFP authentication system can be used to help protect a user's datathat may be stored or input into an electronic device. An NFPauthentication system can be used to help comply with the laws andregulations of medical data that are in effect in a number of countries.

The NFP data that is generated from micro-motions as a result of auser's neurosystem may be considered a form of medical data. The NFPauthentication system can be implemented to effectively protect thecalibrated NFP data that is stored in an electronic device or elsewherewhen used for the purpose of authentication.

Micro-Motions and Tremors

The NFP is generated in response to micro-motions that are related to atype or form of tremor. A tremor is an unintentional, rhythmic musclemovement that causes an oscillation in one or more parts of a humanbody. Tremors may be visible or invisible to the unaided eye. Visibletremors are more common in middle aged and older persons. Visibletremors are sometimes considered to be a disorder in a part of the brainthat controls one or more muscles throughout the body, or in particularareas, such as the hands and/or fingers.

Most tremors occur in the hands. Thus, a tremor with micro-motions canbe sensed when holding a device with an accelerometer or through afinger touching a touch pad sensor.

There are different types of tremors. The most common form or type oftremor occurs in healthy individuals. Much of the time, a healthyindividual does not notice this type of tremor because the motion is sosmall and may occur when performing other motions. The micro-motions ofinterest that are related to a type of tremor are so small that they arenot visible to the unaided eye.

A tremor may be activated under various conditions (resting, postural,kinetic) and can be often classified as a resting tremor, an actiontremor, a postural tremor, or a kinetic or intention tremor. A restingtremor is one that occurs when the affected body part is not active butis supported against gravity. An action tremor is one that is due tovoluntary muscle activation, and includes numerous tremor typesincluding a postural tremor, a kinetic or intention tremor, and atask-specific tremor. A postural tremor is linked to support the bodypart against gravity (like extending an arm away from the body). Akinetic or intention tremor is linked to both goal-directed and nongoal-directed movements. An example of a kinetic tremor is the motion ofa moving a finger to one's nose, often used for detecting a driver fordriving under the influence of alcohol. Another example of a kinetictremor is the motion of lifting a glass of water from a table. Atask-specific tremor occurs during very specific motions such as whenwriting on paper with a pen or pencil.

Tremors, whether visible or not to the eyes, are thought to originate insome pool of oscillating neurons within the nervous system, some brainstructures, some sensory reflex mechanisms, and/or some neuro-mechanicalcouplings and resonances.

While numerous tremors have been described as either physiologic(without any disease) or pathological, it is accepted that theamplitudes of tremors is not very useful in their classification.However, the frequencies of tremors are of interest. The frequencies oftremors allow them to be used in a useful manner to extract a signal ofinterest and generate a unique NFP for each user.

FIG. 6 illustrates a table of various tremor types, their activationcondition, and the expected frequency of motion due to the given tremortype.

Numerous pathological conditions like Parkinson (3-7 Hz), cerebellardiseases (3-5 Hz), dystonias (4-7 Hz), various neuropathies (4-7 Hz)contribute motions/signals to the lower frequencies, such as frequenciesat 7 Hertz (Hz) and below. Because pathological conditions are notcommon to all users, these frequencies of motions/signals are not usefulfor generating NFPs and are desirable to filter out. However, some ofthe embodiments disclosed herein are used to specifically focus on thosepathological signals as a way to record, monitor, follow saidpathologies to determine health wellness or degradation.

Other tremors, such as physiological, essential, orthostatic, andenhanced physiological tremors can occur under normal health conditions.These tremors are not pathologies per se. Accordingly, they are oftenpresent in the population as a whole. Physiological tremors, as well asothers that are common to all users, are of interest because theygenerate micro-motions at frequencies over a range between 3 to 30 Hz,or 4 to 30 Hz. They may be activated when muscles are used to supportbody parts against the force of gravity. Accordingly, holding anelectronic device in one's hand to support the hand and arm againstgravity can generate physiological tremors that can be sensed by anaccelerometer. Touching a touch pad of an electronic device with thefinger of a hand and supporting it against gravity, can generatephysiological tremors that can be readily sensed by a finger touch padsensor.

Essential tremors of a kinetic type, may occur and be sensed when a userhas to enter a PIN or login ID to gain access to a device or a phone.The frequency range of essential tremors is between 4 to 12 Hz thatcould be reduced to a frequency range of 8 to 12 Hz to avoid sensing fortremors that are due to uncommon pathological conditions.

For the physiological tremor (or the enhanced physiological tremor, idemwith larger amplitudes), the coherence of different body sides is low.That is, a physiological tremor on the left body side is not verycoherent to a physiological tremor on the right body side. Accordingly,it is expected that tremors in the left hand or finger will differ fromtremors in the right hand or right finger of a user. Accordingly, theNFP authentication system will require a user to be consistent in usingthe same side hand or finger for authentication; or alternatively,multiple authorized user calibration parameter sets, one for each handor one for each finger that will be used to extract an NFP.

Motions with a higher frequency of interest may be considered to benoise. Accordingly, signals with a frequency higher than the maximum indesired range (e.g., 12 Hz or 30 Hz) in the raw motion signal aredesirous to be filtered out. Thus, a frequency signal range from 8 Hz to12 Hz, and/or 8 Hz to 30 Hz contains useful information regardingmicro-motions that can be used to generate NFPs.

The raw signal, captured by a finger touch pad sensor in an electronicdevice or by an accelerometer of a hand held electronic device, can havea number of unwanted signal frequencies in it. Accordingly, a type offiltration having a response to filter out signals outside the desiredfrequency range can be used to obtain a micro-motions signal from theraw electronic signal. Alternatively, an isolation/extraction means forsignals in the desired frequency range may be used to obtain amicro-motions signal from the raw electronic signal. For example, afinite impulse response band-pass filter (e.g., pass band of 8 to 30 Hz)can be used to select the low signal frequency range of interest in araw electronic signal sensed by a touch pad or accelerometer.Alternatively, a low-pass filter (e.g., 30 Hz cutoff) and a high-passfilter (e.g., 8 Hz cutoff) or a high-pass filter (e.g., 8 Hz cutoff) anda low-pass filter (e.g., 30 Hz cutoff) can be combined in series toachieve a similar result.

Electronic Device with NFP Authentication

Referring now to FIG. 7, a functional block diagram of an electronicdevice 700 with NFP authentication is illustrated. The electronic device700 may be a smart cellular telephone, for example, or other hand heldtype of portable or mobile electronic device for which access control isdesirable. The electronic device 700 includes one or morethree-dimensional (3-D) sensors that may be used to capture raw 3-Delectronic signals that include raw 3-D micro-motion signals that can beused for NFP authentication.

The electronic device 700 may include a processor 701, a storage device(SD) 702, a power source 703 (e.g., rechargeable batteries), abutton/pad 704, a keypad 705, a three-dimensional (3-D) accelerometer706 (optionally), a display device 707, one or more wireless radios 708,and one or more antennae 709 coupled together. If the electronic devicedoes not have a three-dimensional (3-D) accelerometer 706, one or moreof the button/pad 704, the keypad 705, and the display device 707 aretouch sensitive so they may be used to capture 3-D raw electronicsignals that include the 3-D micro-motion signals. In the case theelectronic device 700 is a smart phone, the electronic device includes amicrophone and a speaker coupled to the processor. The microphone may beused to capture a voice sample as an additional means of authentication.

The storage device 702 is preferably a non-volatile type of storagedevice that stores data, instructions, and perhaps other information(e.g., user calibration parameters) in a non-volatile manner so it isnot lost when the electronic device goes to sleep to conserver power oris fully powered off. The storage device 702 stores softwareapplications instructions 712, NFP instructions and possibly NFP data714 for the NFP authentication system, and user scratch pad data 716 forthe user. The NFP instructions and NFP data 714 is separate from theuser scratch pad data 716 and the software application instructions 712for security reasons to make it is inaccessible to all users. Thestorage device 702 is coupled to the processor 701 so that data andinstructions can be read by the processor and executed to performfunctions of software applications. The NFP instructions and NFP data714, if any, is read by the processor to execute the NFP authenticationcontroller module and perform the functions needed to provide NFPauthentication.

The one or more radios 708 are coupled to and between the processor 701and the one or more antennae 709. The one or more radios 708 canwirelessly receive and transmit date over wireless networks. The one ormore radios 708 may include a Wi-Fi radio for local wireless (Wi-Fi)networks, a cellular radio for cellular telephone networks, and bluetooth radio for Bluetooth wireless connections. Software applicationsmay be executed by the electronic device that require authentication toa remote server. The NFP authentication system is used to grant accessto the electronic device itself. However, the NFP authentication systemmay also be used by software applications to verify or authenticate theidentity of an authorized user.

To recharge batteries or provide an alternate power source, a powerconnector and or a combined power/communication connector (e.g.,universal serial bus connector) 722 may be included as part of theelectronic device 700. The connector 722 may couple to the processor 701for data communication and to the power source 703 to recharge batteriesand/or provide an alternate power source for the electronic device 700.For wired connectively, the electronic device 700 may further includenetwork interface controller and connector 724, such as an Ethernetcontroller and port connector (e.g., RJ-45), coupled to the processor.

More portable or mobile electronic devices now include 3-Daccelerometers 706. The 3-D accelerometers 706 have traditionally beenused to determine orientation of the electronic device. However, the 3-Daccelerometer 706 can also be used to capture the micro-motions of auser's hand that holds the electronic device 700. In this case, the 3-Daccelerometer data is captured by the accelerometer 706, sampled,pre-processed, and then provided to the NFP authentication controller.

If the accelerometer is unavailable in an electronic device, either oneor both of the button/pad 704 and keypad 705 are touch sensitive inthree dimensions including X, Y, and Z to use for NFP authentication.The Z axis is the axis perpendicular to the button and keypad along withpressure may be exerted by a finger to select the underlying function ofthe button or key of the keypad to control the electronic device. Thebutton/pad 704 may be a power button for example to power the electronicdevice on/off, for example. The button/pad 704 may be a home button forexample, that brings the electronic device to a “home” or initial userinterface state.

Either or both of a touch sensitive button/pad 704 and/or a touchsensitive keypad 705 may be used to capture three dimensions of rawmicro-motion signals that can be used for NFP authentication.

If the display device 707 is a touch sensitive display device, thekeypad 705 or button 704 may be displayed on the touch sensitive displaydevice 707 from which the three dimensions of raw micro-motion signalsmay be captured.

Portions of an NFP authentication controller (see NFP authenticationcontroller 810A-810C, 810 in FIGS. 8A-8C, 14A) may be formed by theprocessor 701 executing instructions recalled from the firmware/software712 stored in the storage device 702.

Referring now to FIG. 8A, a functional block diagram of a portion of anNFP authentication system with a three-dimensional touch sensitivebutton/pad 704 is shown. The NFP authentication system further includesan NFP authentication controller 810A coupled to the touch sensor 801 ofthe three-dimensional touch sensitive button/pad 704.

The three-dimensional touch sensitive button/pad 704 includes a 3D touchsensor 801 nearest the surface of the pad 704 to sense the position andchanges in the X, Y finger position and finger pressure Z. Thethree-dimensional touch sensitive button/pad 704 typically includes afunctional button switch 814 that is used to generate a function controlsignal that is sent to the processor. The 3D touch sensor 801 generatesthe raw three-dimensional displacement signals that includes themicro-motions indicated by the 3D arrow 850 that are sensed at theuser's finger 890.

After some preprocessing, the NFP authentication controller 810Areceives the data samples representing the micro-motions sensed by thetouch sensor 801 and generates the NFP for the user. In response to theNFP and stored user calibration parameters that were trained by andassociated with the authorized user, the NFP authentication controller810A classifies the NFP and generates a match percentage value. Inresponse to the match percentage value and a predetermined acceptablematch percentage, the NFP authentication controller 810A can generate anaccess grant signal 1449 that grants access to the electronic device forthe authorized user that is touching the touch sensor.

Referring now to FIG. 8B, a functional block diagram of an NFPauthentication system with a three-dimensional touch sensitive keypad705 is shown. The touch sensitive keypad 705 includes a matrix of an Mby N array of 3D touch sensors 801AA-801MN each of which can sense theposition and changes in the X, Y finger position and finger pressure Zof a finger 890 and generate the raw micro-motions data. Each pad mayinclude a functional button switch 814 to concurrently generate aplurality of functional control signals while the raw micro-motionssensor data is being captured by the 3D touch sensors 801AA-801MN.

The signals from M by N array of 3D touch sensors 801AA-801MN ispreprocessed and sampled with the sampled data being coupled to the NFPauthentication controller 810B. The NFP authentication controller 810Bacts similar to the NFP authentication controller 810A in grantingaccess. However, the training of the NFP authentication controller 810Bmay be made to support slightly different signals that can be expectedfrom the different touch sensors in the matrix of the M by N array of 3Dtouch sensors 801AA-801MN.

Referring now to FIG. 8C, a functional block diagram of an NFPauthentication system with a three-dimensional accelerometer 706 in anelectronic device 700 is shown. A users hand 899 holds the electronicdevice 700 that includes the three-dimensional accelerometer 706. Thethree-dimensional accelerometer 706 is coupled to the NFP authenticationcontroller 810C.

While holding the electronic device 700 steady in his/her hand 399, themicro-motions indicated by the 3D arrow 852 in the user's hand 899 aresensed by the 3D accelerometer 706. As the electronic device 700 held inthe users hand is moved about to adjust positions, such as from a pocketto the user's ear, undesirable macro-motions are also sensed by the 3Daccelerometer 706 in addition to the micro-motions. As further explainedherein, these undesirable macro-motions are to be suppressed, filteredout, or eliminated in the desired signal.

The raw three-dimensional accelerometer signals generated by the 3Daccelerometer 706 are preprocessed and undergo compensation due to theforce of gravity. The raw three-dimensional accelerometer signals aresampled into data samples of a dataset so that digital signal processingcan be used to digitally filter and perform digital transformations witha digital processor, such as processor 701 shown in FIG. 7. The datasamples of the accelerometry data are coupled into the NFPauthentication controller.

The NFP authentication controller 810B receives the data samplesrepresenting the micro-motions sensed by the accelerometer 706 andgenerates the NFP for the user. In response to the NFP and stored usercalibration parameters that were trained by and associated with theauthorized user, the NFP authentication controller 810C classifies theNFP and generates a match percentage value. In response to the matchpercentage value and a predetermined acceptable match percentage, theNFP authentication controller 810C can generate an access grant signal1449 that grants access to the electronic device for the authorized userthat is holding the electronic device.

Signal Processing

Referring now momentarily to FIG. 14A, to obtain an NFP 1460 from thesampled micro motion signals 1450 captured by a sensor and sampled by asampler (A-to-D converter), a number of signal processing steps areperformed by the processor in the electronic device. The signalprocessing steps performed on each dimension of the sampled micro motionsignals 1450 are performed by the signal processing and featureextraction module 1401 of the NFP authentication system 810. These oneor more signal processing algorithms performed by the module 1401 may begenerally be referred to herein as the NFP algorithm and method.

Generally, a sequence of events are performed to obtain an NFP for auser and then evaluate its authenticity.

Raw data files (in the X, Y and Z direction) over a predetermined sampleperiod are obtained from 3D accelerometers, or one or more touch padsensors as the case may be.

The raw data files are sampled over predetermined time spans (e.g., 5,10, 20 or 30 seconds) with a predetermined sampling frequency to capturesignals of interest that is compatible with the further filtering needed(e.g., 250 Hz (4 msec between samples), 330 Hz, 200 Hz, or down to 60Hz, twice the 30 Hz frequency of interest).

Signal processing is performed on the sampled signals to generate amicro-motions signal with specific frequency components of interest. Thesampled signals are filtered using a band pass filter the frequencyrange between 7-8 Hz, 7-12 Hz, or 8-30 Hz. This frequency range ofmicro-motions signals is most useful in distinguishing users from oneanother. The band pass filter can also suppress large amplitude signalsdue to voluntary or un-voluntary movements that may be captured by asensor, such as an accelerometer. If an accelerometer is used as thesensor, the influence of the gravitational force is compensated orremoved by signal processing. If an accelerometer is used as the sensor,the sampled signals are made position invariant or orientation invariantby signal processing. The micro-motions signal is a position invariantsampled signal that can be consistently used to extract values offeatures for comparison between users.

Additional signal processing is performed on the micro-motions signal togenerate a signal processed waveform signal from which values for an NFPcan be extracted. Predetermined features are selected for which valuesare to extracted to represent the NFP. The values for an NFP may beextracted directly from the signal processed waveform signal, from eachmicro-motions signal, and/or from both after additional signalprocessing is performed. Regardless, unique values are extractedrepresenting a unique NFP for a user that will differ from other NFPsgenerated by other users.

The user's NFP may be used in a number of different applications. Matchresult values (e.g., percentages) are generated using a classifier(various data mining techniques can be used as the classifier). Theclassifier is trained/calibrated with an initial NFP (a calibration NFP)generating authorized user calibration parameters so that a calibrationmatch result level is achieved. Thereafter, the classifier may be usedin a user mode with the authorized user calibration parameters. Theclassifier in the user mode generates match results values toauthenticate a user as an authorized user or not. An authenticationcontroller, in response to a predetermined access match level, candetermine if a user is an authorized user or not based on the matchresults value.

A number of these signal processing steps are further elaborated herein.

Signal Sampling

Referring now to FIG. 9, a hand acceleration waveform 900 of a handacceleration signal for a single axis (X, Y, or Z) is shown over time. Aportion 901 of the hand acceleration waveform 900 is magnified aswaveform 900T as shown. While analog signal waveforms may be shown inthe drawings, it is understood that analog signal waveforms may besampled over time and represented by a sequence of digital numbers atdiscrete periodic time stamps (a “digital waveform”). While anaccelerometer senses acceleration over time, if a sensor sensesdisplacement over time instead, it may be converted into acceleration bytwice differentiating the displacement signal with time.

The hand acceleration for each axis is sampled over a predeterminedsample time period 905, such as 10, 20 or 30 seconds time spans forexample. The sampling frequency is selected so that it is compatiblewith the filtering that follows. For example, the sampling frequency maybe at 250 Hz (4 milliseconds between samples). Alternatively, thesampling frequency can be 330 Hz or 200 Hz, for example. The samplingmay be performed on an analog signal by a sampling analog to digitalconverter to generate the samples S1-SN represented by a digital numberover the time stamps T1-TN during the given predetermined sample timeperiod. Assuming a 20 second sample time period and a sampling frequencyof 250 Hz, a dataset for acceleration would include 3 (3 axis) times5000 samples over the time period for a total of 15 k samples.

Signal Normalization for Un-Correlated Signals

The generation of the NFP is based on un-correlated signals in threedimensions. The 3D accelerometer to sense tremors is part of theelectronic device that is held by a user's hand. As shown in FIG. 10A,the device 700 and the device axes Xd, Yd, Zd can be held by the user'shand at different orientations with respect to the world W and worldaxes Xw, Yw, Zw. Accordingly, the raw sensor data from the 3Daccelerometer for the device axes Xd, Yd, Zd is correlated. Thus, theraw sensor data from the 3D accelerometer for the device axes Xd, Yd, Zdis dependent on the orientation of the electronic device 700. A signalnormalization process is performed using principal component analysis(PCA) to make the 3D accelerometer data for each axisorientation-invariant or rotation-invariant and thus un-correlated.

Each dataset of raw sensor data from the 3D accelerometer for the deviceaxes Xd, Yd, Zd over a given sample time period is analyzed in the3D-feature space. Three eigenvectors and eigenvalues are determined foreach axis. The eigenvalues of each eigenvector are compared to determinethe largest eigenvalue that identifies the largest eigenvector. Thepoints of the data sets are then rotated (points and their data valuesare transformed in space) so that the largest eigenvector aligns withand along a predetermined axis. The predetermined axis is a constant forall datasets of 3D raw sensor data originating from the same electronicdevice 700. The predetermined axis may even be a constant for any devicethat implements the NSP algorithm with a 3D accelerometer. Thepredetermined axis may be the Zw world axis for example. Aligning thelargest eigenvector of each dataset along the predetermined axistransforms the X,Y,Z components of 3D points in the raw sensor data sothat they are uncorrelated and rotation-invariant.

For example, FIG. 10B illustrates a 3D point 1011A of acceleration withrespect to an eigenvector 1010A. The data set that forms the eigenvector1010, including the point 1011A is rotated in 3D space to eigenvector1010B that aligns with the Zw world axis. The point 1011A and its X,Y,Zcomponent values are transformed in 3D space to point 1011B and itsX′,Y′,Z′ component values as a result.

Signal Suppression/Extraction/Filtering

This NFP algorithm specifically extracts tremors or micro-motions fromthe transformed sensor data signals that are linked with the motorcontrol of the brain cortex, its subcortical parts, cerebellum, centralnervous system influenced or not by peripheral structures (e.g.,muscular, skeletal, glandular, etc.) and suppresses unwanted parts ofthe transformed sensor data signals.

Accordingly, it is desirable to generate or extract a micro-motionssignal from the transformed hand acceleration signal. However, thetransformed hand acceleration signal may have a number of undesirablesignals within it that can suppressed, removed, or filtered out. Forexample, a hand holding the electronic device is often moved around withlarge movements, such as by moving from ones pocket/purse or pocket sothat a keypad is accessible and a display screen is visible.

Large swings 902 in acceleration can occur due to such large movementsas is shown in FIG. 9. In some cases, these large swings in a signalfrom a user's large motions (macro-motions) are useful as a behaviorprofile, such as described in U.S. patent application Ser. No.13/823,107 filed by Geoff Klein on Jan. 5, 2012, entitled METHOD ANDSYSTEM FOR UNOBTRUSIVE MOBILE DEVICE USER RECOGNITION. However, becausethese large swings have little to do with micro-motions associated witha neuromuscular tremor, in this case it is desirable to suppress orremove these large swings during signal processing and the generation ofa unique NFP for a user.

Unwanted signal components such as attributed to large swings 902 can besuppressed. The large swings may be from vibrations linked to buildingsor structures where a user is located, or from the motions that the useractually executes while holding the electronic device with theaccelerometer sensor.

Most buildings and structures resonate at a frequency between 3 Hz and 6Hz. Accordingly, the vibrations from buildings and structures areoutside the desirable range of frequencies (e.g., outside the range of 8Hz to 30 Hz) and are unlikely to resonate and contaminate the desirablesignals with the range. Signals in the range of 3 Hz and 6 Hz are to besubsequently filtered out.

The large scale motions (macro-motions) of a user include jumping aroundwith the electronic device, moving one's arm with the electronic device,turning around with the electronic device, walking running, jogging, andother large body movements with the electronic device. The large scalemotions (macro-motions) of a user are generally not useful in thegeneration of NFPs. The NFP algorithm is not based on hands or bodyattitudes, nor is the NFP algorithm based on motionsrepertoires/libraries.

The large scale motions (macro-motions) of a user form large scalesignal swings 902 shown in FIG. 9 for example. The large scale motionsof a user are not likely to be repeated when trying to regenerate an NFPduring the authentication process. Large scale motions of a user mayunfavorably skew an algorithm or calculation in the generation of anNFP. Accordingly, large scale signals from large scale motions arepartially suppressed or filtered out from the acquired data by the bandpass filtering that is performed for the desired range of frequencies.The large scale signals from the large scale motions are highlycorrelated over relatively long time periods. The three dimensions ofmicro-motions due to the neurological system are not well correlated.Accordingly, subsequent signal processing to de-correlate the threedimension of signals will suppress the large scale signals from thelarge scale motions.

Alternate ways of suppressing/filtering the large scale signals, due tothe large scale motions of a user, out from the desired signal may alsobe used. The electronic signals may be analyzed and thenclassified/identified as small signals and large signals which areseparated out from the small signal amplitude of the micro-motions. Theanalysis may be of the form described in “Time Series ClassificationUsing Gaussian Mixture Models of Reconstructed Phase Spaces” by RichardJ. Povinelli et al., IEEE Transactions on Knowledge and DataEngineering, Vol. 16, No. 6, June 2004. Alternatively, a separation ofthe large signals due to voluntary motion may be made by using aBMFLC-Kalman filter as is described in “Estimation of PhysiologicalTremor from Accelerometers for Real-Time Applications” by Kalyana C.Veluvolu et al., Sensors 2011, vol. 11, pages 3020-3036, attached heretoin the appendix.

The magnified hand acceleration waveform 900T is more representative ofan acceleration signal that includes tremors from which a micro-motion(micro-acceleration) signal of interest may be generated. The waveform900T has a number of frequency components that are outside the frequencyrange of interest. For example, the frequency range of interest in thesignal is from 8 Hz to 12 Hz, and/or from 8 Hz to 30 Hz. These frequencyranges are associated with known tremors that more human beings shouldcommonly have.

Referring now to FIG. 11A, a band pass filter (BPF) is shown having afilter response with a lower cutoff (LC) frequency and an upper cutoff(UC) frequency at the ends of the desired frequency range. The BPF is inthe form of a digital finite impulse response (FIR) band pass filter tofilter a digital signal. This BPF will filter out undesirable frequencysignal components from the raw electronic signal captured by the sensorto generate a micro-motion acceleration signal of interest in thedesired frequency range. Three BPFs may be used in parallel, one foreach axis signal. Alternatively, one BPF may be time shared between eachaxis.

Referring now to FIG. 11B, a digital high-pass filter (HPF) with the LCfrequency and a digital low-pass filter (LPF) with the UC frequency canbe combined in series to achieve a similar result to the digital FIRband pass filter in the generation of the micro-motion accelerationsignal of interest. Alternatively, a low-pass filter with the UCfrequency and a high-pass filter with the LC frequency can be combinedin series to achieve a similar resultant signal output.

Additional signal processing are to be performed with the processor onthe micro-motions signal to generate the NFP.

Gravity Correction for Accelerometer Sensor

It is desirable to remove the influence of the force of gravity on thethree-dimensional accelerometry data captured by the accelerometer. Thisis so the NFP that is generated is substantially independent of deviceorientation. A dedicated three-dimensional touch pad sensor may not beinfluenced by the force of gravity. In which case, these steps need notbe followed for three-dimensional data that is captured by a touch padsensor when a user finger is touched against it.

The acceleration of gravity is a constant. Accordingly, the influence ofthe gravitation force is on the frequency spectrum is expected to be afixed vector near zero frequency. The gravitation force acts as a zerofrequency DC component so one would expect it would effectuate a leakagetowards low frequencies in the signals (below 1 Hz).

Referring now to FIG. 12A, a high pass filter at a cutoff frequencybelow 1 Hz (e.g., 0.25 Hz) may be used to compensate for the force ofgravity and improve the accuracy of the recognition with an NFP. Amicro-motion signal with the effects of gravity are coupled into thegravity high pass filter (HPF) having a high pass filter response with acutoff (CO) frequency less than 1 Hz but greater than zero Hz. Thesignal output from the gravity HPF is the micro-motions signal withoutthe effects of gravity.

Referring now to FIG. 12B, another way to eliminate the influence of thegravitation force is by transforming the coordinates of the 3Dacceleration data points (ADP1-ADPN) each data set. The XYZ coordinatesfor the center of gravity CG in the sampled datasets of 3D accelerationpoints (X, Y, Z signals) over the predetermined sample period is firstdetermined. Then, the coordinates of the 3D acceleration points in thegiven dataset undergo a translation transform so that the center ofgravity is at the (X=0,Y=0,Z=0) coordinate or origin point of axes. Thewhole dataset undergoes a translation transform in this case.

Yet another way to get rid of the influence of the gravitation force inthe acceleration signals would be to analytically correct a gravitationforce vector for the phase shift that it induces in the 3D accelerometrydata. The gravitation force vector can be determined from the raw 3Dacceleration signals. Then, the 3D accelerometry data can be dampened inone direction in response to the gravitation force vector andstrengthened in the opposite direction of the gravitation force vector.

Filtered Micro-Motions Waveform

After the filtering and suppression to remove unwanted signal andtransformation to compensate for gravity or correlation, the desiredmicro-motions signal is formed.

Referring now to FIG. 13A, a waveform diagram shows three axes of rawacceleration waveform data 1301-1303 that are offset from each other dueto gravitation forces and differences in orientation. FIG. 13Billustrates acceleration waveform data for three axes aftersuppression/removal/filtering of unwanted signals from the capturedacceleration sensor data. The acceleration waveform data in FIG. 13B isrepresentative of the micro-motions signal waveforms. If a 3D touchsensitive sensor is used instead of a 3-D accelerometer, a displacementwaveform in three axes may be the resultant signal. While FIGS. 13A-13Bshow a sample of four seconds, sample periods of more or less time maybe used.

There are three axis for the micro-motions signal corresponding to thethree axis of acceleration sensed by the 3D accelerometer or the threeaxis of displacement sensed by the 3D touch pad sensor. The three axisof the micro-motions signal define points in three dimensions x(t),y(t), z(t) that can be plotted. The three dimensions of micro-motionsignals x(t), y(t), z(t) over the sample period of time for a user canbe further processed into phase(t), y(t), z(t) and plotted in athree-dimensional Poincare phase plot diagram such as shown in FIGS.2A-2C.

The three-dimensional Poincare phase plot diagrams for the differentusers show that the micro-motion signals have unique patterns that canbe used to identify and authenticate a user. A unique pattern for eachuser can be obtained from the Poincare phase plot diagram. Anotherunique pattern for each user could be obtained from the time series ofthe micro-motions themselves, without any phase information. However, itis easier to use a signal processor and digital signal processingalgorithms to extract a unique pattern from the signal itself.

NFP Authentication Controller

Referring now to FIG. 14A, a block diagram of an NFP authenticationcontroller 810 is shown. The NFP authentication controller 810 includesa signal processing and feature extractor module 1401 (may be split intotwo separate modules), an NFP authentication classifier module, and anauthorization controller module 1404. The NFP authentication controller810 may further include an optional secondary authentication module 1406for multifactor authentication, such as by a keypad. One or more of themodules may be implemented by software/firmware instructions executed bya processor, hardwired electronic circuits, or a combination of each.

The NFP authentication controller 810 may further include a non-volatilestorage device 1454 coupled to the classifier 1402 and the authorizationcontroller 1404 to store data such as user calibration parameters 1466;access match (AM) level 1456A, mandatory recalibration (MR) level 1456B,voluntary recalibration (VR) level 1456C (collectively referred to asmatch percentage levels 1456); and an authentication enable bit (EN)1455. Alternatively, the non-volatile storage device 1454 may beexternal to the NFP authentication controller 810 but remain internal tothe electronic device as a secured independent non-volatile storagedevice or a secured part of a larger non-volatile storage device.

The NFP authentication controller 810 receives the three dimensions ofthe micro-motions data samples 1450 in each data set for each sampleperiod. The micro-motions data samples 1450 are coupled into the signalprocessing and feature extractor module 1401. The signal processing andfeature extractor module 1401 performs signal processing and signalanalysis on the micro-motions data samples 1450 to extract a pluralityof extracted features 1460X, 1460Y, 1460Z for each of respective thethree dimensions (X, Y, Z). The extracted features 1460X,1460Y,1460Zcollectively represent the NeuroFingerPrint (NFP) 1460 that is coupledinto the NFP authentication classifier module 1402.

The NFP authentication classifier module 1402 receives the NFP 1460, theextracted features from the micro-motions data samples 1450, andgenerates a match percentage (MP) signal output 1465. In a user mode,the match percentage signal 1465 is coupled into the authenticationcontroller 1404. In a calibration or training mode, the match percentagesignal 1465 is used by a processor in the electronic device to evaluatethe match percentage signal 1465 in response to selections for theinitial user calibration parameters 1466. It is expected thattraining/calibration of the NFP authentication classifier module and thegeneration of the initial user calibration parameters 1466, last between5 and 10 seconds. It is expected that in the user mode, that it takeless than 5 seconds to sense motion in a body part of a user anddetermine that access be granted or denied.

In the calibration or training mode, an authorized user may generate oneor more sets of user calibration parameters 1466 so that the NFPauthentication system operates under different conditions. For example,a user may want to hold the electronic device in either hand and haveaccess granted. The tremors will be different between left and righthands or between different fingers. The user may want to calibrate theNFP authentication system to both left and right hands or to a pluralityof fingers. Moreover, in some cases, more than one user will be using anelectronic device. In which case, multiple individuals may be authorizedusers and multiple calibrations will need be made and stored. Thus, thestorage device 1454 may store a plurality of sets of user calibrationparameters for the same authorized user or different authorized users.

The authentication enable bit (EN) 1455 is coupled into theauthentication controller 1404. The authentication enable bit (EN) 1455may be used to enable the authentication controller 1404, subsequent tothe initial calibration or training mode. After initial calibration ortraining mode, the authentication enable bit (EN) 1455 is set and theauthentication controller 1404 is enabled. The authentication enable bit(EN) 1455 is not reset in the user mode or a recalibration mode afterthe initial calibration or training mode. Unless the entire electronicdevice is erased, along with the enable bit, the authenticationcontroller 1404 is enabled by the enable bit 1455 for enhanced security.

In the user mode, the match percentage signal 1465 is used by theauthentication controller 1404 to evaluate whether or not to grantaccess into the electronic device and its software applications. Thematch percentage signal 1465 is evaluated against the access match level1456A to generate the access granted (AG) signal 1499. If the matchpercentage signal 1465 is greater than or equal to the access matchlevel 1456A, the access granted (AG) signal 1499 is generated at a logiclevel to signal access is granted. If the match percentage signal 1465is less than the access match level 1456A, the access granted (AG)signal 1499 is not generated and access is not granted. The accessgranted (AG) signal 1499 is coupled to the processor in order to enablethe authorized user to control and operate the functions of theelectronic device.

If the optional secondary authentication module is used, a secondarymatch (SM) signal 1468 is coupled into the authentication controller1404. In this case, the authentication controller 1404 further evaluatesthe secondary match signal 1468 on whether or not to generate the accessgranted (AG) signal 1499. The authentication controller 1404 may use ANDlogic to require two conditions be met before the access granted signalis generated. Alternatively, the authentication controller 1404 may useOR logic to require two conditions be met before the access grantedsignal is generated.

In response to an inactive reactivate signal 1470 from the processor,the authentication controller 1404 maintains the access granted signal1499 in an active state for so long as the user uses the electronicdevice and avoids a sleep state or a time out to enter a protectedstate. If the sleep state or time out occurs, the reactivate signal 1470is pulsed by the processor. In response to the pulsed reactivate signal,the authorization controller 1404 inactivates the access granted signal1499 so that a user must re-authenticate himself/herself with the NFPauthentication system of the electronic device to gain access.

Referring now to FIG. 15, a graph of match percentage levels (MP) 1456is shown in comparison with the match percentage signal 1465 along the Xaxis. The Y axis indicates the generation of the access granted (AG)signal 1499 by the authentication controller 1404 and whether or accessis to be granted to an alleged user.

In the user mode, the match percentage signal 1465 is used by theauthentication controller 1404 to evaluate whether or not to grantaccess into the electronic device and its software applications. Thematch percentage signal 1465 is evaluated against the access match level1456A. If the match percentage signal 1465 is at or above the accessmatch level 1456A the access granted (AG) signal 1499 is generated bythe authentication controller 1404. If the match percentage signal 1465is below the access match level 1456A, the access granted (AG) signal1499 is not generated by the authentication controller 1404.

Referring now to FIGS. 14A and 15, the authentication controller 1404may also generate one or more recalibration signals to inform the userto recalibrate the NFP authentication system by regenerating the usercalibration parameters 1466 for the authorized user, before the NFPauthentication classifier 1402 fails to generate a level of the matchpercentage signal 1465 at or above the access match level 1456A.

In the training or calibration mode, the NFP authentication classifier1402 is trained/calibrated by initial user calibration parameters 1466so that the NFP authentication classifier 1402 generates a level of thematch percentage signal 1465 at a calibration level 1457 that may be ator near 100%, such as 98%. After training, in the user mode, the NFPgenerated by a user over time can shift as his/her body ages, disease,or other reason that affects the physiological condition of the body.Accordingly, the match percentage signal 1465 may decrease from thecalibration level 1457 as time passes. Periodic recalibration is used toreset the user calibration parameters 1466 so that the match percentagesignal 1465 is brought back to the calibration level 1457. Periodicrecalibration may be required more or less often of a user dependingupon the user's age, health, and other physiological conditions of thebody.

In the user mode, the NFP authentication classifier 1402 is used by theauthentication controller 1404 to evaluate whether or not recalibrationis needed. The determination of recalibration is responsive to themandatory recalibration (MR) level 1456B and the voluntary recalibration(VR) level 1456C. The mandatory recalibration (MR) level 1456B is lessthan both the voluntary recalibration (VR) level 1456C and thecalibration level 1457. The voluntary recalibration (VR) level 1456C isless than the calibration level 1457.

Recalibration requires that the user first be verified as an authorizeduser, access must be first granted to the electronic device to enter theuser mode and recalibrate. If the device is stolen by an unauthorizeduser, the unauthorized user will not be granted access to recalibratethe device.

If the match percentage signal 1465 falls below the voluntaryrecalibration (VR) level 1456C, a voluntary recalibration signal 1471 isgenerated by the authentication controller 1404. In this case, theelectronic device informs the user through its user interface, thathe/she should pause and take a moment to voluntarily recalibrate the NFPauthentication system by regenerating the user calibration parameters1466. It is expected that most users would voluntarily choose torecalibrate the NFP authentication system. However, there will be someauthorized users who will choose to wait, forget the warning, or ignoreit entirely.

For those authorized users that do not voluntarily recalibrate the NFPauthentication system, they may be forced to undergo a mandatoryrecalibration process. If the match percentage signal 1465 furtherdecreases and falls below the mandatory recalibration (MR) level 1456B,a mandatory recalibration signal 1472 is generated by the authenticationcontroller 1404. In this case, after the authorized user is verified bythe NFP authentication classifier 1402 and granted access by theauthentication controller 1404, the electronic device immediately goesinto a recalibration mode informs the user through its user interface toprepare and perform the recalibration procedures by continuing to holdthe device appropriately or touch a button appropriately. The user isfurther informed that the NFP authentication system is performingrecalibration and to wait until the recalibration is completed and theNFP authentication system has been successfully recalibrated with theregeneration of the user calibration parameters 1466 to generate thematch percentage signal 1465 at or above the calibration level 1457.

If the electronic device is not used for some time, it is possible thatthe match percentage signal 1465 further decreases and falls below theaccess match (AM) level 1456C. In which case, the authorized user may bedenied access. Accordingly, selection of the value for the access match(AM) level 1456C is important so that the authorized users are notreadily denied access to the electronic device while unauthorized usersare denied access. It is desirable to make the generation of the NFPsignal 1460, generated by the module 1401 and coupled into the NFPauthentication classifier 1402, less sensitive to variances of a user(e.g., time-aging, disease) so that the recalibration is less frequent.Accordingly, it is desirable to select signal processing and featureextraction algorithms of the module 1401 so that the sensitivity in thegeneration of the NFP is low and it is less likely to change over time.

Example settings of the match percentage levels (MP) 1456 in increasingorder is 85% for the access match (AM) level 1456A, 90% for themandatory recalibration (MR) level 1456B, and 95% for the voluntaryrecalibration (VR) level 1456C. Thus, a user is informed by the userinterface of the electronic device to perform a voluntary recalibrationbefore being required to perform a mandatory recalibration.

With the mandatory recalibration being required of the user, it shouldavoid the NFP authentication system from denying access to theelectronic device if it is being actively used. If the electronic devicesits for some time (e.g., one or more years), a user may be required towipe the electronic device clean, re-initialize the NFP authenticationsystem, and reload applications and/or data, such as from a backup.

Referring now to FIGS. 14A-14B, a model for an NFP authenticationclassifier 1402 is shown. The model is a regression analysis model thatmay be linear in accordance with one embodiment. In alternateembodiment, the regression analysis model for the NFP authenticationclassifier 1402 may be non-linear. During a training or calibrationmode, features of the micro-motion signals from an authorized user areextracted by the signal processing and feature extraction module 1401 togenerate a calibration NFP 1460, including calibration NFP information1460X,1460Y,1460Z for each axis.

The calibration NFP information 1460X,1460Y,1460Z for each axis isplaced into a single row NFP matrix 1452. Appropriate values ofauthorized user calibration parameters 1466 are unknown. The values foruser calibration parameters 1466 can be randomly set into a singlecolumn calibration matrix 1454. Matrix multiplication is performed bythe processor to multiply the single row NFP matrix 1452 and the singlecolumn calibration matrix 1454 together to get a match percentage value1465.

During calibration/training, the processor searches out the usercalibration parameters 1466 that are to be placed in the single columncalibration matrix 1454 to generate a predetermined value for the matchpercentage 1465. This predetermined value is referred to as thecalibration level 1457. For example, the calibration level 1457 may beset to 90%. In which case, the processor searches out values for theauthorized user calibration parameters 1466 such that when multipliedwith the NFP 1460, the match percentage 1465 output from the classifieris 90% or greater.

Once the authorized user calibration parameters 1466 are set and storedin the electronic device, the NFP classifier 1402 uses the authorizeduser calibration parameters 1466 to multiply against subsequentregenerated NFPs that are generated from an unknown user or theauthorized user.

If a person cannot subsequently generate a regenerated NFP that issubstantially similar to the calibration NFP, when the user calibrationparameters 1466 are multiplied against a differing regenerated NFP, thevalue of the match percentage 1465 generated by the classifier 1402 willbe low. The authorization controller 1404 can be set so that low valuesfor the match percentage 1465 deny access to unknown persons whogenerate a significantly different regenerated NFP.

If a person subsequently generates an NFP, a regenerated NFP that issubstantially similar to the calibration NFP, when the user calibrationparameters 1466 are multiplied against the regenerated NFP, the value ofthe match percentage 1465 generated by the classifier 1402 will be high.The authorization controller 1404 can be set such that high values forthe match percentage 1465 above an access match level 1456A grantsaccess to an electronic device.

Signal Processing and Feature Extraction to Generate NFPs

Referring now to FIG. 13A, a raw unfiltered sample of acceleration datain three dimensions from a 3D accelerometer is shown. For X, Y, and Zaxes of the electronic device, raw signals 1301-1303 are captured foreach of the three dimensions. With some preprocessing, band passfiltering, and compensation for gravity, micro-motion signal waveforms1311-1313 shown in FIG. 13B can be respectively formed from the rawsignals 1301-1303. Further signal processing can then be performed onthe micro-motion signal waveforms 1301-1303 to form an NFP for the givensample of acceleration data. There are various ways to generate an NFPfrom the micro-motions signal. Once calibrated/trained, the NFPauthentication controller in an electronic device uses a consistentmethod of generating the NFP.

The micro-motion signal in three dimensions is related to a commontremor that is found in users. There is a pattern hidden in the timedomain of the micro-motions signal that is unique to each user as it isoriginating in his/her neuromuscular functioning. It is desirable toemphasize the pattern by performing signal processing on themicro-motions signal and then extracting features therefrom to form anNFP. A calibration NFP can then be used to generate authorized usercalibration parameters during a training or calibration process. Theauthorized user calibration parameters can be subsequently used toclassify regenerated NFPs and distinguish the known authorized user fromunknown unauthorized users for the purpose of user authentication.

However, before an NFP can be used for authentication, it is used totrain or calibrate a model of a classifier by generating a set ofauthorized user calibration parameters. This set of authorized usercalibration parameters are then subsequently used with the classifiermodel to evaluate future NFPs (regenerated NFPs) that are captured bysensors from unknown persons. Various signal processing algorithms canbe used to extract data from the micro-motions signal as the NFP.

Hidden patterns in the micro-motions signal, such as the micro-motionsignals waveforms 1311-1313 shown in FIG. 13B for example, may bedetected with an inverse spectral analysis. The inverse spectralanalysis is performed in order to recover unknown signal componentslinked to time variance.

One type of inverse spectral analysis that may be used is a Cepstrumanalysis. Cepstrum analysis is a tool for the detection of periodicityin a frequency spectrum. It can be used to detect repeated patterns,their periodicities, and frequency spacing.

Generally, a CEPSTRUM is the result of taking the Inverse Fouriertransform (IFT) of the logarithm of the estimated spectrum of a signal.

As shown by the equation below, a power CEPSTRUM of a signal may bedefined as the squared magnitude of the inverse Fourier transform of thelogarithm of the squared magnitude of the Fourier transform of a signal.

Power CEPSTRUM of signal=|

⁻¹{log(|

{f(t)}²)}|²

Initially, a Fourier spectrum of the micro-motions signals, such as themicro-motions signals shown in FIG. 13, is taken using a Fast Fouriertransform. Mathematically this may be accomplished by performing aFourier Transform using the equation

{tilde over (f)}(ξ)=∫_(−∞) ^(∞) f(x)e ^(−2πixξ) dx,

where ξ represents real frequency (in hertz) values and the independentvariable x represents time), the transform variable. Using software ordigital circuits that can digitally sample a signal, a discrete-timeFourier Transform (DFT) may be used represented by the equation

$X_{k} = {\sum\limits_{n = 0}^{N - 1}\; {{x\lbrack n\rbrack} \cdot {^{{- }\; 2\; \pi \; \frac{kn}{N}}.}}}$

The result of Fourier transform on the micro-motions signal is thespectral density (power spectrum or Fourier spectrum). FIG. 16illustrates an example of a spectral density taken on acceleration overtime for a tremor at the hand. Note that the expected peak (P) signalcomponent of a physiological tremor is at a peak frequency (PF) ofaround 10 to 12 Hz. However, there is still much hidden pattern (HP)information in the spectra density curve for the tremor that is useful.

The spectral density (power spectrum) is a composite of the componentfrequencies of a signal. That is, there are numerous signal frequenciesthat are composed together to form the spectral density curve. It isdesirable to effectively separate them out to show a pattern. Thecomposite of signals illustrated by the spectral density is aconvolution of signals equivalent to multiplications of the constitutivesignals.

Taking the logarithm of a convolution effectively turns it into asummation of the constitutive signals instead of a multiplication.Additionally, a logarithm of the spectral density (or a logarithm of thesquare of spectral density) emphasizes the lower amplitude frequencycomponents in the spectra density curve where hidden pattern (HP)information may be found. Taking a logarithm of the spectral densityeffectively compresses the large signal amplitudes in the spectraldensity signal and expands the smaller amplitudes in the spectraldensity signal.

However, it is difficult to see the periodicity and patterns in thesignal waveform generated by a logarithmic transform. Accordingly, aninverse Fourier transform (IFT) is performed on the logarithm signalwaveform so that the unknown frequency component and hidden patterns arevisible. The inverse Fourier transform of the logarithmic transformseparates the components of the composite signal in the spectra densityof the micro-motions signal waveforms.

FIGS. 17A-17B illustrate a CEPSTRUM taken on the micro-motions waveformsshown in FIG. 13B. This resultant waveform that is generated aftertaking the inverse Fourier transform (IFT) of the Log of the spectrum.The CEPSTRUM generates a substantially resolved series of the unknownsignal components in the resultant waveform. Values for a pattern offeatures can be extracted from each axis (dimension) in the resultantwaveform and used as an NFP to identify a user. Note that the horizontalaxis of the CEPSTRUM waveform shown in FIGS. 17A-17B is quefrency andnot a measure of time in the time domain.

Generally, the inverse Fourier transform is an integral that canintegrate a function g over values of its variable. The inverse Fouriertransform in this case may be represented by the equation:

⁻¹ g(x):=

_(n) e ^(2πix−ξ) g(ξ)dξ.

To recover a discrete time data sequence x[n], an inverse discreteFourier transform (IDFT) may be used.

$\begin{matrix}{{x\lbrack n\rbrack} = {T{\int_{\underset{T}{1}}{{{X_{1\text{/}T}(f)} \cdot ^{\; 2\; \pi \; {fnT}}}\ {f}}}}} & {\left( {{integral}\mspace{14mu} {over}\mspace{14mu} {any}\mspace{14mu} {interval}\mspace{14mu} {of}\mspace{14mu} {length}\mspace{14mu} 1\text{/}T} \right)} \\{{\frac{1}{2\; \pi}{\int_{2\; \pi}{{{X_{2\; \pi}(\omega)} \cdot ^{{\omega}\; n}}\ {{\omega}.}}}}\;} & {\left( {{integral}\mspace{14mu} {over}\mspace{14mu} {any}\mspace{14mu} {interval}\mspace{14mu} {of}\mspace{14mu} {length}{\mspace{11mu} \;}2\pi} \right)}\end{matrix}$

Using an Inverse Fast Fourier Transform (IFFT) the equation becomes

${{IFFT}_{N}\left( {n,F} \right)} = {{\sum\limits_{k = 0}^{N - 1}\; {{F(k)}^{{+ j}\; 2\; \pi \; {nk}\text{/}N}}} = {\sqrt{N}{f(n)}}}$

where a sequence of N samples f(n) are indexed by n=0 to N−1, and theDiscrete Fourier Transform (DFT) is defined as F(k), where k=0 to N−1.

${F(k)} = {\frac{1}{\sqrt{N}}{\sum\limits_{n = 0}^{N - 1}\; {{f(n)}^{{- j}\; 2\; \pi \; {kn}\text{/}N}}}}$

FIGS. 17A-17B illustrate an example of a three dimensional CEPSTRUMwaveform signal 1700. The waveform and signal 1700 is the result ofperforming a CEPSTRUM analysis on the micro-motions signal resulting inCEPSTRUM signal waveforms 1701,1702,1703, one for each dimension in themicro-motions signal. The CEPSTRUM waveform signal 1700 has distinctfeatures associated with it that can be readily used to identify a user.For example, the first N peaks of the CEPSTRUM waveform 1700 may thepredetermined features to extract from each CEPTSTRUM waveform 1700 toidentify a user. The peaks are chosen for their high variance so theyare distinct features. For each user, the values of quefrency andamplitude for the first N peaks can be used as the respective user'sNFP. The values of quefrency and amplitude for the N peaks are can beused as the NFP that is input into the classifier model to generate thematch percentage (MP), in response to authorized user calibrationparameters.

Other features can be selected and their values extracted from theCEPSTRUM of the micro-motions signal. Other signal processing may beperformed so that additional features or patterns in the micro-motionssignal become apparent and may be used in the generation of an NFP.Consistency in what features are used to extract values is key for anNFP authentication system. The features should be predetermined. Thesame features (e.g., first N peaks) should be used during acalibration/training mode with a calibration NFP and when in a use modeand forming the regenerated NFPs from new sample sets.

For example, in FIG. 17A it can be seen that the first five peaks P1,P2, P3, P4, P5 at their respective quefrequencies and amplitude on eachaxis (e.g., 3×5 total amplitude values from 3 axes/curves and 5 peaks)have a large variance that may be used to distinctly identify a user.The amplitudes and quefrencies for the first five peaks P1, P2, P3, P4,P5 will be significantly different when captured from a different user.Thus, the values of the amplitudes and quefrencies extracted for thefirst five peaks P1, P2, P3, P4, P5 can be used to distinguish theidentity of users, such as shown in FIGS. 18A-18B for example. While thefirst five peaks are used in this example, it is not a constraint or alimitation on the embodiments as fewer peaks, additional peaks, or aplurality of other features may be selected to form NFPs.

With the first five peaks P1, P2, P3, P4, P5 being the predeterminedfeature used to extract values from the CEPSTRUM signal waveform, theNFP can be regenerated over and over again for the same user with minorvariation. If a different user generates his/her NFP, the extractedvalues for the predetermined features will substantially vary. Thesubstantial variance between NFPs of an authorized user and a differentuser can be used to grant access to the authorized user and deny accessto the different user.

The first N features extracted from a CEPSTRUM waveform can be used toregenerate an NFP and used by the NFP authentication classifier toclassify matches with authorized user calibration parameters. Selectingearlier features in the CEPSTRUM, such as the first N (e.g., N being 3to 5) peaks or other coefficients, may decrease the sample period butstill represent much of the variance in the signal. These first Ncoefficients can be expanded into 128 extracted features by projectingthe N features by PCA analysis, and hence become linear combination of128 extracted features, for example.

Over a sample period of 5, 10, or 20 seconds, the values extracted fromthe CEPSTRUM waveform are the positions (quefrencies) and theirrespective amplitudes of the predetermined feature. If the predeterminedfeatures are the first five peaks for example, the values of amplitudeand quefrency for each axis are extracted as the NFP. This methodaggregates time over a sample period.

Instead of aggregating time over the sample period, the CEPSTRUM may beanalyzed in near-real time. In this case for example, one peak at onequefrency may be selected and plotted as a function of time. Forexample, peak P1 may be selected at a quefrency of 8 in FIG. 17A. The 3Dcoordinates for amplitude of each axis at the quefrency of 8 can then beplotted as a function of time. Other features at other quefrencies canbe further selected so there are N frequencies (e.g., 128 ‘quefrencies’)so an n-space analysis can be performed using a nearest neighbor, alinear or quadratic analysis where n is 128 for example.

However, an analysis of 128 quefrencies is a challenge. The analysis canbe made simpler and ease computing time, by looking at where the mostvariance is in a waveform. We can take the first 3, 5, or 10quefrequency positions and perform a principle component analysis (PCA)in 3, 5, or 10 dimensions of data space. A nearest neighbor analysis canthen be performed, for example, on this restricted data space to extractvalues for the NFP.

Another way of feature extraction and generating values for the NFP canbe done without 3D plotting of data points per quefrency as a functionof time. One can take quefrency, amplitude and time as true coordinatesof a vector. N quefrencies can be selected to plot amplitude and time asdimensions. These vectors can then be plotted for resolved frequenciesat any given time. This generates a time-series of those extractedfeatures that can be analyzed to extract values for the NFP.

Another way of feature extraction and generating values for the NFP isto extract those frequencies (quefrencies), amplitudes at various times(like 50 times a second remembering that the interesting and relevantraw signals are bandpassed between 16 and 30 plus Hz). Within a fewseconds, a series of numbers are generated that may be used as extractvalues for the NFP. Values at different time points instead of quefrencymay be extracted and used as the NFP.

While CEPSTRUM signal processing is described herein, other signalprocessing algorithms can be used on the micro motion signals so thatother patterns appear in a different waveform. Different features can beextracted from that waveform signal and used to define an NFP for theNFP authentication classifier. The different extracted features can beused as the NFP that is input into a model to generate the matchpercentage (MP) in response to authorized user calibration parameters.

Referring now to FIGS. 18A-18B and 19A-19B, examples of a single axis ofdifferent NFPs for different users are shown. Additional dimensions maybe provided by the two or more additional dimensions.

FIGS. 18A-18B illustrate examples of different NFPs for a single axisbased on quefrency for two different users. One or more of the fiveextracted peaks P1-P5 occur at different quefrencies F1-F5 withdifferent amplitudes in each of FIGS. 18A-18B. Accordingly, the NFP ofeach user differs. FIGS. 19A-19B illustrate examples of different NFPsfor a single axis based on time for two different users. One or more ofthe five peaks P1-P5 occur at different times T1-T5 with differentamplitudes in each of FIGS. 19A-19B. Accordingly, the NFP of each userdiffers.

The first five peaks of the CEPSTRUM signal waveform are thepredetermined features to be extracted for each user from the differentmicro-motion signals of each user. The values for placement andamplitude of the first five peaks distinguish one user from another,similar to how teeth and cuts in a door key distinguish keys. Theauthorized user calibration parameters act somewhat like tumblers of alock that engage the teeth and cuts in a door key. If the teeth and cutsin the door key are wrong, the tumblers of the door lock will not beproperly engaged to unlock the lock. With more than one dimension (e.g.,3 axis) for feature extraction, the more unique the NFPs become. FIG.14B illustrates an added dimension AD 1460A of values of features thatare extracted over and above the three dimensions of features extractedfrom the micro-motions signal 1460X,1460Y,1460Z to form the NFP 1460.

While the 3D acceleration or 3D displacement values may be used, timemay be another dimension added. At one point in time, the 3D values withhave one amplitude while at another point in time they will haveanother.

Training/Calibration

Referring now to FIG. 14B, one or more dimensions for an NFP 1460, anduser calibration parameters (if trained) are coupled into the NFPauthentication classifier 1402. The algorithm for the NFP authenticationclassifier is a data mining algorithm, such as a linear regressionalgorithm, a non-linear regression algorithm, a linear quadraticalgorithm, a quadratic equation algorithm, a nearest neighborclassifier, or a k by N nearest neighbor classifier. A linear regressionalgorithm is shown with a one row matrix 1452 with xM elements orcolumns multiplied together with a one column matrix 1454 with xM rows.The one row matrix 1452 represents the NFP 1460 with its values for theextracted features for each of the one or more dimensions. The onecolumn matrix 1454 represents the authorized user calibration parameters1466. The multiplication in this case provides a value for a matchpercentage. During training/calibration, the user calibration parameters1466 are adjusted to form a calibration level as the match percentage(MP) output 1465.

The NFP authentication classifier 1402 is trained/calibrated before useby an authorized user. Once the initial training/calibration iscompleted, unauthorized users cannot train or recalibrate the classifier1402. Micro-motions signals are captured during one or more sampleperiods by the accelerometer with the electronic device in a user's handor with the user pressing a touch sensor. The calibration may occur withleft or right hands or both hands for an accelerometer. The calibrationfor a touch sensor may occur with one or more fingers of each hand.

The micro-motion signals are processed using signal processingalgorithms and features are extracted out of the resultant waveform asNFP calibration samples, referred to herein as the calibration NFP.During training/calibration, the NFP calibration samples are used tofind authorized user calibration parameters 1454 associated with theauthorized user and train/calibrate the classifier to generate a desiredcalibration match percentage level. After the authorized usercalibration parameters 1454 have been generated and securely storedaway, the calibration NFP is discarded for security reasons. By notsaving the calibration NFP, an NFP needs to be regenerated in the usermode, referred to as the regenerated NFP, for a user to gain access tothe electronic device. After device authentication has been reenacted tolock the device from access, the NFP needs to be regenerated by theauthorized user.

The regenerated NFP is generated with another sample of theneuro-mechanical micro-motions from the user and by recalling the storedauthorized user calibration parameters from a storage device. Theregenerated NFP is then used for authentication purposes to gain accessto the electronic device. With the regenerated NFP, the authorized usercalibration parameters are likely to provide a high match percentagesuch that access is likely to be granted once again to the electronicdevice. Like the calibration NFP is discarded, the regenerated NFP isnot saved after being temporary used to measure authentication. Theregenerated NFP is subsequently discarded for security reasons and thenext regenerated NFP is generated and its match percentage with theauthorized user calibration parameters evaluated. If the regenerated NFPis classified as having a match percentage greater than or equal to theaccess match level, access to the electronic device granted.

The training of the NFP authentication classifier may be linear or not(i.e., non-linear) depending upon the algorithm used by the classifier1402.

To calibrate the classifier 1402, a predetermined value is selected forthe calibration match percentage level. The predetermined value for thecalibration match percentage level may be 100% or less. However, thepredetermined value for the calibration match percentage level should beabove the desired grant access match (AM) level.

Given the NFP calibration samples, the processor in the electronicdevice runs through test sequences of user calibration parameters inorder to generate a match percentage equal to or greater than thepredetermined value for the calibration match percentage level. Beforeselecting the initial user calibration parameters that generate a matchpercentage equal to or greater than the calibration match percentagelevel, the user may be asked to generate additional micro-motions(representing the NFP) by continuing to hold the device or continue topress the touch sensitive button. These additional NFPs are used toverify that the classifier is properly trained/calibrated by the usercalibration parameters 1454.

After calibration, experiments were run to determine error rates ingranting access to authorized users. Using a CEPSTRUM signal analysis ofthe micro-motions signal, a trained classifier in a hand held electronicdevice with accelerometer sensors had an error rate of 7% in recognizingthe authorized user. An authorized user was correctly recognized 93% ofthe time. Accordingly, the access match (AM) level needs to be set below93%, such as 80% for example, so that the authorized user isconsistently authorized to access the electronic device.

When the CEPSTRUM signal analysis of the micro-motions signal was usedwith a touch sensitive keypad in an electronic device, error resultsseem to improve. When a small sample size of users typed their PINnumber on a touch sensitive keypad, a one hundred percent recognitionrate of the authorized user was achieved by the method using CESTRUMsignal analysis. Regardless, the access match (AM) level needs to be setbelow 100%, such as 85% for example, so that the authorized user isconsistently authorized to access the electronic device.

Alternatives to Cepstrum Signal Processing and Generating NFPs.

Many existing time-domain approaches to the task of signalclassification are based on the existence of a fairly simple underlyingpattern, or template, known a priori or learned from the data. With realsignals however—like cardiac, speech, or electric motor systems—such asimple pattern rarely exists because of the complexity of the underlyingprocesses. Indeed, frequency-based techniques are based on the existenceof spectral patterns. From a stochastic processes perspective,frequency-based techniques of signal processing used on themicro-motions signal will just capture the first and second ordercharacteristics of the system.

The CEPSTRUM analysis described herein can extract some time-relatedinformation from the accelerometer data, but this approach remainslinked to the frequency dispersion. Because there is more information inthe micro-motion signals than plain frequencies, other signal processingtechniques and feature extraction methods may be able to improve thestrength of an NFP so that it is less sensitive and less likely tochange over time.

Chaos analytics may be used instead for example to generate NFPs. Usingchaos, one could extract analogous predetermined features to thatdescribed with respect to the CEPSTRUM. However, these predeterminefeatures are not based on frequencies, but with trajectories in time.Example features for extraction using Chaos analytics is the center ofgravity of the strange attractor(s), combined with their fractaldimension, and Lyapunov exponent. Data sweeps over several seconds(e.g., 5, 10, 20 seconds) may be used to obtain values for thesefeatures from the micro-motions signal.

Another example of using chaos analytics is to use the waveform signalgenerated from the Logarithm of the spectrum for each axis without theIFT. From the logarithm waveform signal, amplitude and time can be takenas true coordinates of a vector and plotted for N (e.g., N equals 128)resolved frequencies at any given time. The centers, dimensionality andlyapunov, may be extracted as a time series of values representing theNFP.

In accordance with one embodiment, phase plots or Poincare plots (seePoincare plots 200A-200D shown in FIGS. 2A-2C for example) may begenerated from which NFP signal features can be extracted and coupledinto the NFP authentication classifier. There is more information in anorbit of a phase plot or Poincare plot than that of the Cepstrumwaveforms. Accordingly, it is useful to use phase plots or Poincareplots, generated from the filtered micro-motions data-stream, from whichto extract the features in the sensor data for an NFP.

In yet another embodiment, a hidden Markov model analysis is used as thesignal processing module to obtain more feature information as the NFPto couple into the chosen classifier. A Markov model is a stochasticmodel used to model randomly changing systems where future states dependonly on the present state and not on the sequence of events thatpreceded it. A Markov chain, a state space of one or more states, isoften used as the Markov model. The transition from one state to anotheris a memory less random process. The next state depends only on thecurrent state and not on the sequence of events that preceded it. Ahidden Markov model is a Markov chain for which the state is onlypartially observable.

Referring now to FIG. 20, a state machine 2000 is illustrated thatimplements the hidden Markov model analysis. The state machine 2000 andhidden Markov model are used to extract features from a signal processedwaveform of the micro-motions signal and perform the classification togrant or deny access (or authenticate a user).

The state machine 2000 includes states 2001-2004 and extracted featuresF1 2010A through Fn 2010N from a signal processes waveform (e.g., thePoincare plots shown in FIGS. 2A-2D) where N is less than or equal to10. State 2001 is an accept state, state 2002 is a recalibration state,state 2003 is a deny state, and state 2004 is a reset/initializationstate.

There are state transitions that can be made between the states2001-2004 of the state machine 2000. State transition a1 is from theaccept state 2001 to the recalibration state 2002. State transition a2is from the recalibration state 2002 to the deny state 2003. Statetransition a3 is from the recalibration state 2002 to the accept state2001. State transition a4 is from the accept state 2001 to the denystate 2003. The deny state 2003 may also transition to thereset/initialization state 2004.

From the states 2001-2003 to the features F1 2010A through Fn 2010N,there are output probabilities. From the accept state 2001 to thefeatures F1 2010A through Fn 2010N, there are output probabilities Oa1through Oan. From the recalibration state 2002 to the features F1 2010Athrough Fn 2010N, there are output probabilities Or1 through Orn. Fromthe deny state 2003 to the features F1 2010A through Fn 2010N, there areoutput probabilities Od1 through Odn.

With a hidden Markov model, one tries to get at correct answers withouthaving the inner details of the system that is being analyzed. Theanswers are the states 2001-2003, which are truly complex and based onchaotic inputs. The N features F1 2010A through Fn 2010N that areextracted are the only measurable. The features may be various, such asa center of gravity, or a series of Lyapunov exponents, whatever can beextracted out of data trajectories, each of which be related withspecific odds ratios (e.g., Oa1-Oan, Or1-Orn, Od1-Odn) to the threestates 2001-2003. One exits the state machine 2002 through thetransition to the fourth state 2004. The fourth state 2004 correspondsto the system re-initialization, such as a full reset.

In still another embodiment, a dynamical systems signal analysis isperformed based on reconstructed phase spaces (RPSs) with new signalclassification approaches. Dynamical invariants may be used as thefeatures that are to be extracted after the signal analysis. It isexpected that this approach can capture more information leading tobetter user recognition.

Method of Use

As shown in FIGS. 8A-8C, a person holds the electronic device or touchesone or more touch sensitive pads to authenticate himself/herself. For afew seconds of a sample period, a 3D sensor (3 sensors located ondifferent axes) in the electronic device senses the vibrations ormicro-motions in the user's hand or finger due to the physiologicalcondition of the user's body. The 3D sensor generates three electronicsignals concurrently in time that can be sampled and converted intodigital form. The digital samples are preprocessed and analyzed usingsignal processing techniques to generate an NFP in real time for theuser that just handled the electronic device or touched the touchsensor.

In response to authorized user calibration parameters, the NFP may thenbe used to evaluate whether or not the person holding or touching theelectronic device is an authorized user. In response to authorized usercalibration parameters, the NFP may be used as a key to encrypt anddecrypt data files. Alternatively, a valid NFP may be used toauthenticate a file was signed, written, or created by the authorizeduser.

The user calibration parameters may be stored in a local file or in alocal storage device in the electronic device. Alternatively, the usercalibration parameters may be stored in a remote file in a storagedevice associated with a server or in a storage device of a storage areanetwork, in the internet cloud. The authorized user calibrationparameters are useless without the hand or finger of the live authorizeduser. Without the hand or finger of the authorized user regeneratinghis/her NFP, access to the electronic device is denied.

Applications for NFP Authentication

The NFP Authentication system can be used to control access into anelectronic device. The NFP authentication system may also be used tocontrol access to functions associated with and software applicationsexecuted by the electronic device. The NFP authentication system can beused to control access to a remote electronic device (i.e., where thesensing is done on a local device to determine access to a remoteelectronic device.

Examples of functions that may have controlled access by the NFPauthentication system include but are not limited to logons,user-protected accesses, e-transactions, and any other local functionthat requires positive authentication.

For example security applications for an NFP authentication systemincludes computer and software logins, electronic commerce, electronicbanking, and anti-fraud applications. Examples of human controlapplications for an NFP authentication system include domoticauthentication and protection (home security systems), car safety, andprofessional access to restricted zones. Examples of medicalapplications for an NFP authentication system include a diagnostic aidfor health professionals (Neuromuscular a.o.), therapy monitoring forpatients, and patient and doctor medical authentication (records, datarouting, . . . ) into databases storing medical records. Examples ofhealth and wellness applications for an NFP authentication systeminclude securing physical fitness records of a user. Examples of gamingapplications for an NFP authentication system include safety featuresfor virtual reality gaming and/or simulators.

Biometric encryption combines touch recognition technology with theactual data storage. The data is encrypted with the NFP so that it isun-readable except by the authorized user/owner of the data. Backdooraccess is unavailable. However, other authorized users that are trustedpersons may also have authorized user calibration parameters generatedso that they are granted access in case of problems related to theoriginal user/owner.

Touch recognition technology can be embedded in certain applications.For example, touch recognition technology can be used in access controlpanels such that when a surface is touched by human hands or a humanfinger, a door may be unlocked so that access to a vehicle, a building(e.g., a home or office); or a zone (e.g., a floor) may be granted ordenied in response to the user's NFP without using a key (keyless).Similarly, touch recognition technology can be used in a wireless keyfob held in the user's hand or touched by a finger so that access may begranted or denied in response to the user's NFP without using a key.Touch recognition technology can be combined with fingerprintauthentication. In this case, an image scanner and touch sensitive padcan be used together to concurrently capture both a user's fingerprintand the user's NFP for multiple authentications in granting or denyingaccess.

With additional signal processing algorithms, the generation of the NFPmay be adapted to monitor a user's health and wellness. The change inthe regeneration of the NFP can decrease the match percentage indicatinga degradation in health of a user. It may be used to discover and/ormonitor neurodegenerative diseases such as Alzheimer, Parkinson, or beused for therapeutic monitoring. It may be used to obtain measures ofheart rhythm variability, the correlation of stress and emotionalsituations can be measured with accelerometers by extracting the shockwaves of the cardiac pulses when a user holds a device. It may be usedas a physiological safety feature for virtual reality glasses and gogglethat may be used, such as in the video game industry.

Governments may provide online services, such as voting, and the touchtechnology can be used to verify the identity of an authorized user.Schools and educators may provide online tests. Touch technology may beused to verify the identity of the student taking an online test or evena test that may be taken at school. Electronic online banking can bemade more secure with banks being able to verify the identity of itscustomers with the touch technology.

The NFP technology can protect medical records by better authenticationof a patient and doctor as well as encryption of the medical records. Auser can authenticate his/her interactions with the health care system(providers, insurers, medical record companies . . . ). A user cansecurely store his/her medical records in the cloud or on any device inan encrypted format with the NFP being the key.

Advantages of NFP Authentication

There are a number of advantages to touch recognition with a NFP. Touchrecognition does not require a centralized database. Access to anelectronic device is locally controlled and shielded by a local NFP userauthentication system.

An additional advantage of NFP is that one can be uniquely identified asa proper user of an electronic device but remain anonymous, providingstrong user privacy protection. The NFP is generated in response tobrain/nerve system related signals generated by a sensor. It can providenext to foolproof unique user identification (even twins have differentmotricity).

Touch-recognition employs a neurological algorithm to generate an NFPfrom a micro-motion signal associated the micro motions of a finger on ahand. This avoids context-linked repertoires or motion repertoires thatare associated with behavioral biometrics. It is user-friendly and canprovide a friction-less login and authentication.

The neurological algorithms can be developed in software and added topre-existing electronic devices with an application programminginterface (API) or a mobile software development kit. It can be added aspart of a system login.

The neurological algorithms need not continuously monitor a sensor. Theycan be sleeping until the electronic device is woken up from sleep orthe sensor senses a touch. Training time and identification is quick,only a few seconds needed. Accordingly, the neurological algorithms canconserver power and intelligently use the battery power that is usuallyavailable in mobile device electronics.

CONCLUSION

When implemented in software, the elements of the embodiments areessentially the code segments of instructions that may be executed byone or more processors to perform and carry out tasks and providefunctionality. The program or code segments can be stored in a processorreadable medium or storage device that are coupled to or at least incommunication with the one or more processors. The processor readablemedium may include any medium or storage device that can storeinformation. Examples of a processor readable medium include, but arenot limited to, an electronic circuit, a semiconductor memory device, aread only memory (ROM), a flash memory, an erasable programmable readonly memory (EPROM), a floppy diskette, a CD-ROM, an optical disk, ahard disk, or a solid state drive. The program or code segments may bedownloaded or transmitted between storage devices, for example, overcomputer networks such as the Internet, Intranet, etc.

While this specification includes many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations of the disclosure. Certain features that aredescribed in this specification in the context of separateimplementations may also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation may also be implemented in multipleimplementations, separately or in sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination may in some cases be excised from the combination, and theclaimed combination may be directed to a sub-combination or variationsof a sub-combination.

Accordingly, while certain exemplary embodiments have been particularlydescribed and shown in the accompanying drawings, they should not beconstrued as limited by such embodiments, but rather construed accordingto the claims that follow below.

1. A method to locally authenticate an authorized user and controlaccess to an electronic device, the method comprising: generating aneuro-mechanical fingerprint for a user in response to a micro-motionsignal sensed at a body part of the user using the electronic device;generating a match percentage of the neuro-mechanical fingerprint inresponse to authorized user calibration parameters; and controlling useraccess to the electronic device in response to the match percentage. 2.The method of claim 1, wherein the neuro-mechanical fingerprint is a setof values for a plurality of unique features extracted from themicro-motion signal associated with neuro-muscular micro-motions in thebody part of the user.
 3. The method of claim 2, wherein the body partof the user is a finger.
 4. The method of claim 2, wherein the body partof the user is a hand.
 5. The method of claim 1, wherein the matchpercentage is generated without needing a calibration neuro-mechanicalfingerprint previously used to generate the user calibration parameters.6. The method of claim 1, wherein if the match percentage is greaterthan or equal to an access match level, the user is identified as anauthorized user and the authorized user is granted access to theelectronic device.
 7. The method of claim 6, further comprising:informing the authorized user to voluntarily recalibrate the authorizeduser calibration parameters in response to the match percentage beingless than or equal to a voluntary recalibration level.
 8. The method ofclaim 7, further comprising: recalibrating the authorized usercalibration parameters in response to the authorized user selecting tovoluntary recalibrate the authorized user calibration parameters,wherein the voluntary recalibration level has a greater match percentagethan the access match level.
 9. The method of claim 7, furthercomprising: recalibrating the authorized user calibration parameters inresponse to the match percentage being less than or equal to aninvoluntary recalibration level, wherein the involuntary recalibrationlevel has a match percentage greater than the access match level and amatch percentage less than the voluntary recalibration level.
 10. Themethod of claim 6, further comprising: sensing the micro-motion signalat the body part of the user.
 11. A method comprising: extracting afirst neuro-mechanical fingerprint (NFP) associated with an authorizeduser, generating authorized user calibration parameters associated withthe authorized user in response to the first neuro-mechanicalfingerprint; extracting a second neuro-mechanical fingerprint associatedwith an unknown user or the authorized user; determining a matchpercentage of the second neuro-mechanical fingerprint in response to theauthorized user calibration parameters; and controlling access to anelectronic device in response to the match percentage.
 12. The method ofclaim 11, wherein the first NFP is a calibration NFP and the second NFPis a regenerated NFP associated with the authorized user such thataccess to the electronic device is granted to the authorized user inresponse to the match percentage.
 13. The method of claim 12, whereinthe calibration NFP is a first set of values for a plurality of uniquefeatures extracted from a first micro-motion signal associated with theneuro-muscular micro-motions in a hand of the authorized user; and theregeneration NFP is a second set of values for the plurality of uniquefeatures extracted from a second micro-motion signal associated with theneuro-muscular micro-motions in the hand of the authorized user.
 14. Themethod of claim 12, wherein the calibration NFP is a first set of valuesfor a plurality of unique features extracted from a first micro-motionsignal associated with the neuro-muscular micro-motions in a finger ofthe authorized user; and the regeneration NFP is a second set of valuesfor the plurality of unique features extracted from a secondmicro-motion signal associated with the neuro-muscular micro-motions inthe finger of the authorized user.
 15. The method of claim 11, whereinthe first NFP is a calibration NFP associated with the authorized userand the second NFP is a regenerated NFP associated with the unknown usersuch that an attempt to access the electronic device by the unknown useris denied in response to the match percentage.
 16. The method of claim15, wherein the calibration NFP is a first set of values for a pluralityof unique features extracted from a first micro-motion signal associatedwith the neuro-muscular micro-motions in a hand of the authorized user;and the regeneration NFP is a second set of values for the plurality ofunique features extracted from a second micro-motion signal associatedwith the neuro-muscular micro-motions in a hand of the unknown user. 17.The method of claim 15, wherein the calibration NFP is a first set ofvalues for a plurality of unique features extracted from a firstmicro-motion signal associated with the neuro-muscular micro-motions ina finger of the authorized user; and the regeneration NFP is a secondset of values for the plurality of unique features extracted from asecond micro-motion signal associated with the neuro-muscularmicro-motions in a finger of the unknown user. 18-30. (canceled)
 31. Aphysiological authentication controller to control access to anelectronic device, the physiological authentication controllercomprising: a signal processing module to generate a neuro-mechanicalfingerprint for a user; an authentication classifier module coupled incommunication with the signal processing module, the authenticationclassifier module to generate a match percentage in response to theneuro-mechanical fingerprint and authorized user calibration parameters;and an authentication controller module coupled in communication withthe authentication classifier module, the authentication controllermodule to control access to the electronic device in response to thematch percentage and an access match level.
 32. The physiologicalauthentication controller of claim 31, further comprising: a storagedevice coupled in communication with the authentication classifiermodule and the authentication controller module, the storage devicestoring the authorized user calibration parameters and the access matchlevel.
 33. The physiological authentication controller of claim 32,wherein the storage device further stores a recalibration level; and theauthentication controller module causes an authorized user torecalibrate the authentication classifier module with updated authorizeduser calibration parameters in response to the match percentage beingless than or equal to the recalibration level. 34-63. (canceled)